Research

25 pages, 2227 KiB

Open AccessArticle

Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks

by Ruijie Liu and Yajing Huang

Mathematics 2023, 11(6), 1545; https://doi.org/10.3390/math11061545 - 22 Mar 2023

Viewed by 988

Abstract

This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network [...] Read more.

This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network and a stock projected network, whose structural characteristics can intuitively reveal the overlapping portfolios among different shareholders, as well as shareholder allocation structures among different stocks. The degree of nodes in the shareholder projected network obeys the power law distribution, the network aggregation coefficient is large, while the degree of most nodes in the stock projected network is small and the network aggregation coefficient is low. Furthermore, the two projected networks’ community structures are analyzed, respectively. Most of the communities in the shareholder projected network and stock projected network are small-scaled, indicating that the majority of large shareholders hold different shares from each other, and the investment portfolios of large shareholders in different stocks are also significantly different. Finally, by comparing the stock projected sub-network obtained from the shareholder-stock bipartite sub-network in which the degree of shareholder nodes is 2 and the original stock projected network, the effectiveness of the statistically validated network model, and the community division method on the research of the shareholder-stock bipartite network are further verified. These results have important implications for understanding the investment behavior of large shareholders in the stock market and contribute to developing investment strategies and risk management practices. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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14 pages, 1158 KiB

Open AccessArticle

Identifying Influential Spreaders Using Local Information

by Zhe Li and Xinyu Huang

Mathematics 2023, 11(6), 1302; https://doi.org/10.3390/math11061302 - 08 Mar 2023

Viewed by 1046

Abstract

The heterogeneous nature indicates that different nodes may play different roles in network structure and function. Identifying influential spreaders is crucial for understanding and controlling the spread processes of epidemic, information, innovations, and so on. So how to identify influential spreaders is an [...] Read more.

The heterogeneous nature indicates that different nodes may play different roles in network structure and function. Identifying influential spreaders is crucial for understanding and controlling the spread processes of epidemic, information, innovations, and so on. So how to identify influential spreaders is an urgent and crucial issue of network science. In this paper, we propose a novel local-information-based method, which can obtain the degree information of nodes’ higher-order neighbors by only considering the directly connected neighbors. Specifically, only a few iterations are needed to be executed, the degree information of nodes’ higher-order neighbors can be obtained. In particular, our method has very low computational complexity, which is very close to the degree centrality, and our method is of great extensibility, with which more factors can be taken into account through proper modification. In comparison with the well-known state-of-the-art methods, experimental analyses of the Susceptible-Infected-Recovered (SIR) propagation dynamics on ten real-world networks evidence that our method generally performs very competitively. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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20 pages, 465 KiB

Open AccessArticle

DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction

by Libin Chen, Luyao Wang, Chengyi Zeng, Hongfu Liu and Jing Chen

Mathematics 2022, 10(22), 4193; https://doi.org/10.3390/math10224193 - 09 Nov 2022

Cited by 3 | Viewed by 1633

Abstract

Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The [...] Read more.

Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph embeddings. On the other hand, the overall size of the network tends to accumulate over time, and its growth rate can reflect the ability of the entire network to generate interactions of heterogeneous nodes; therefore, we developed a graph dynamics model to model the evolution of graph dynamics. Moreover, the temporal properties of nodes regularly affect the generation of temporal interaction events with which they are connected. Thus, we developed a node dynamics model to model the evolution of node connectivity. In this paper, we propose DHGEEP, a dynamic heterogeneous graph-embedding method based on the Hawkes process, to predict the evolution of dynamic heterogeneous networks. The model considers the generation of temporal events as an effect of historical events, introduces the Hawkes process to simulate this evolution, and then captures semantic and structural information based on the meta-paths of temporal heterogeneous nodes. Finally, the graph-level dynamics of the network and the node-level dynamics of each node are integrated into the DHGEEP framework. The embeddings of the nodes are automatically obtained by minimizing the value of the loss function. Experiments were conducted on three downstream tasks, static link prediction, temporal event prediction for homogeneous nodes, and temporal event prediction for heterogeneous nodes, on three datasets. Experimental results show that DHGEEP achieves excellent performance in these tasks. In the most significant task, temporal event prediction of heterogeneous nodes, the values of precision@2 and recall@2 can reach 30.23% and 10.48% on the AMiner dataset, and reach 4.56% and 1.61% on the DBLP dataset, so that our method is more accurate at predicting future temporal events than the baseline. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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26 pages, 636 KiB

Open AccessArticle

Influence Maximization under Fairness Budget Distribution in Online Social Networks

by Bich-Ngan T. Nguyen, Phuong N. H. Pham, Van-Vang Le and Václav Snášel

Mathematics 2022, 10(22), 4185; https://doi.org/10.3390/math10224185 - 09 Nov 2022

Cited by 2 | Viewed by 1390

Abstract

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected [...] Read more.

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community’s elements is selected into a seed set holding these bounds. Nevertheless, resolving this problem encounters two main challenges: strongly influential seed sets might not adhere to the fairness constraint, and it is an NP-hard problem. To address these shortcomings, we propose three algorithms (

FBIM 1

,

FBIM 2

, and

FBIM 3

). These algorithms combine an improved greedy strategy for selecting seeds to ensure maximum coverage with the fairness constraints by generating sampling through a Reverse Influence Sampling framework. Our algorithms provide a

(1 / 2 - ϵ)

-approximation of the optimal solution, and require

O (k T log ((8 + 2 ϵ) n \frac{ln \frac{2}{δ} + ln (_{k}^{n})}{ϵ^{2}}))

,

O (k T log \frac{n}{ϵ^{2} k})

, and

O (\frac{T}{ϵ} log \frac{k}{ϵ} log \frac{n}{ϵ^{2} k})

complexity, respectively. We conducted experiments on real social networks. The result shows that our proposed algorithms are highly scalable while satisfying theoretical assurances, and that the coverage ratios with respect to the target communities are larger than those of the state-of-the-art alternatives; there are even cases in which our algorithms reaches

100 %

coverage with respect to target communities. In addition, our algorithms are feasible and effective even in cases involving big data; in particular, the results of the algorithms guarantee fairness constraints. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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18 pages, 1571 KiB

Open AccessArticle

Network Alignment across Social Networks Using Multiple Embedding Techniques

by Van-Vang Le, Toai Kim Tran, Bich-Ngan T. Nguyen, Quoc-Dung Nguyen and Vaclav Snasel

Mathematics 2022, 10(21), 3972; https://doi.org/10.3390/math10213972 - 26 Oct 2022

Cited by 1 | Viewed by 1776

Abstract

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be [...] Read more.

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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19 pages, 456 KiB

Open AccessArticle

Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice

by Bich-Ngan T. Nguyen, Phuong N. H. Pham, Van-Vang Le and Václav Snášel

Mathematics 2022, 10(20), 3772; https://doi.org/10.3390/math10203772 - 13 Oct 2022

Viewed by 1197

Abstract

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer [...] Read more.

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of publications show that the DR-submodular function has wide applications in optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. In this research, we propose two main streaming algorithms for the problem of maximizing a monotone DR-submodular function under cardinality constraints. Our two algorithms, which are called

StrDRS 1

and

StrDRS 2

, have

(1 / 2 - ϵ)

,

(1 - 1 / e - ϵ)

of approximation ratios and

O (\frac{n}{ϵ} log (\frac{log B}{ϵ}) log k)

,

O (\frac{n}{ϵ} log B)

, respectively. We conducted several experiments to investigate the performance of our algorithms based on the budget allocation problem over the bipartite influence model, an instance of the monotone submodular function maximization problem over the integer lattice. The experimental results indicate that our proposed algorithms not only provide solutions with a high value of the objective function, but also outperform the state-of-the-art algorithms in terms of both the number of queries and the running time. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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13 pages, 1702 KiB

Open AccessArticle

Hybrid Fake Information Containing Strategy Exploiting Multi-Dimensions Data in Online Community

by Huiru Cao, Xiaomin Li, Yanfeng Lin and Songyao Lian

Mathematics 2022, 10(18), 3265; https://doi.org/10.3390/math10183265 - 08 Sep 2022

Viewed by 1148

Abstract

It is well-established that, in the past few years, internet users have rapidly increased. Meanwhile, various types of fake information (such as fake news or rumors) have been flooding social media platforms or online communities. The effective containing or controlling of fake news [...] Read more.

It is well-established that, in the past few years, internet users have rapidly increased. Meanwhile, various types of fake information (such as fake news or rumors) have been flooding social media platforms or online communities. The effective containing or controlling of fake news or rumor has drawn wide attention from areas such as academia to social media platforms. For that reason, numerous studies have focused on this subject from different perspectives, such as employing complex networks and spreading models. However, in the real online community, misinformation usually spreads quickly to thousands of users within minutes. Conventional studies are too theoretical or complicated to be applied to practical applications, and show a lack of fast responsiveness and poor containing effects. Therefore, in this work, a hybrid strategy exploiting the multi-dimensional data of users and content was proposed for the fast containing of fake information in the online community. The strategy is mainly composed of three steps: the fast detection of fake information by continuously updating the content comparison dataset according to the specific hot topic and the fake contents; creating spreading force models and user divisions via historical data, and limiting the propagation of fake information based on the content and user division. Finally, an experiment was set up online with BBS (Bulletin Board System), and the acquired results were analyzed by comparison with other methods in different metrics. From the extracted results, it has been demonstrated that the proposed solution clearly outperforms traditional methods. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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18 pages, 885 KiB

Open AccessArticle

Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths

by Yelai Feng, Huaixi Wang, Chao Chang and Hongyi Lu

Mathematics 2022, 10(14), 2521; https://doi.org/10.3390/math10142521 - 20 Jul 2022

Cited by 7 | Viewed by 1411

Abstract

Betweenness centrality evaluates the importance of nodes and edges in networks and is one of the most pivotal indices in complex network analysis; for example, it is widely used in centrality ordering, failure cascading modeling, and path planning. Existing algorithms are based on [...] Read more.

Betweenness centrality evaluates the importance of nodes and edges in networks and is one of the most pivotal indices in complex network analysis; for example, it is widely used in centrality ordering, failure cascading modeling, and path planning. Existing algorithms are based on single-source shortest paths technology, which cannot show the change of betweenness centrality with the growth of paths, and prevents deep analysis. We propose a novel algorithm that calculates betweenness centrality hierarchically and accelerates computing via GPUs. Based on the novel algorithm, we find that the distribution of shortest path has an intrinsic correlation with betweenness centrality. Furthermore, we find that the betweenness centrality indices of some nodes are 0, but these nodes are not edge nodes, and they characterize critical significance in real networks. Experimental evidence shows that betweenness centrality is closely related to the distribution of the shortest paths. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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16 pages, 586 KiB

Open AccessEditor’s ChoiceArticle

Benchmarking Cost-Effective Opinion Injection Strategies in Complex Networks

by Alexandru Topîrceanu

Mathematics 2022, 10(12), 2067; https://doi.org/10.3390/math10122067 - 15 Jun 2022

Cited by 2 | Viewed by 1556

Abstract

Inferring the diffusion mechanisms in complex networks is of outstanding interest since it enables better prediction and control over information dissemination, rumors, innovation, and even infectious outbreaks. Designing strategies for influence maximization in real-world networks is an ongoing scientific challenge. Current approaches commonly [...] Read more.

Inferring the diffusion mechanisms in complex networks is of outstanding interest since it enables better prediction and control over information dissemination, rumors, innovation, and even infectious outbreaks. Designing strategies for influence maximization in real-world networks is an ongoing scientific challenge. Current approaches commonly imply an optimal selection of spreaders used to diffuse and indoctrinate neighboring peers, often overlooking realistic limitations of time, space, and budget. Thus, finding trade-offs between a minimal number of influential nodes and maximizing opinion coverage is a relevant scientific problem. Therefore, we study the relationship between specific parameters that influence the effectiveness of opinion diffusion, such as the underlying topology, the number of active spreaders, the periodicity of spreader activity, and the injection strategy. We introduce an original benchmarking methodology by integrating time and cost into an augmented linear threshold model and measure indoctrination expense as a trade-off between the cost of maintaining spreaders’ active and real-time opinion coverage. Simulations show that indoctrination expense increases polynomially with the number of spreaders and linearly with the activity periodicity. In addition, keeping spreaders continuously active instead of periodically activating them can increase expenses by 69–84% in our simulation scenarios. Lastly, we outline a set of general rules for cost-effective opinion injection strategies. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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27 pages, 4891 KiB

Open AccessArticle

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

by Yedong Shen, Fangfang Gou and Jia Wu

Mathematics 2022, 10(10), 1669; https://doi.org/10.3390/math10101669 - 13 May 2022

Cited by 22 | Viewed by 2336

Abstract

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large [...] Read more.

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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20 pages, 2929 KiB

Open AccessArticle

Influence Maximization Based on Snapshot Prediction in Dynamic Online Social Networks

by Lin Zhang and Kan Li

Mathematics 2022, 10(8), 1341; https://doi.org/10.3390/math10081341 - 18 Apr 2022

Cited by 4 | Viewed by 1572

Abstract

With the vigorous development of the mobile Internet, online social networks have greatly changed the way of life of human beings. As an important branch of online social network research, influence maximization refers to finding

K

nodes in the network to form the [...] Read more.

With the vigorous development of the mobile Internet, online social networks have greatly changed the way of life of human beings. As an important branch of online social network research, influence maximization refers to finding

K

nodes in the network to form the most influential seed set, which is an abstract model of viral marketing. Most of the current research is based on static network structures, ignoring the important feature of network structures changing with time, which discounts the effect of seed nodes in dynamic online social networks. To address this problem in dynamic online social networks, we propose a novel framework called Influence Maximization based on Prediction and Replacement (IMPR). This framework first uses historical network snapshot information to predict the upcoming network snapshot and then mines seed nodes suitable for the dynamic network based on the predicted result. To improve the computational efficiency, the framework also adopts a fast replacement algorithm to solve the seed nodes between different snapshots. The scheme we adopted exhibits four advantages. First, we extended the classic influence maximization problem to dynamic online social networks and give a formal definition of the problem. Second, a new framework was proposed for this problem and a proof of the solution is given in theory. Third, other classical algorithms for influence maximization can be embedded into our framework to improve accuracy. More importantly, to reveal the performance of the scheme, a series of experiments based on different settings on real dynamic online social network datasets were carried out, and the experimental results are very promising. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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22 pages, 11953 KiB

Open AccessArticle

Investigation of Terrorist Organizations Using Intelligent Tools: A Dynamic Network Analysis with Weighted Links

by Alexandros Z. Spyropoulos, Charalampos Bratsas, Georgios C. Makris, Evangelos Ioannidis, Vassilis Tsiantos and Ioannis Antoniou

Mathematics 2022, 10(7), 1092; https://doi.org/10.3390/math10071092 - 28 Mar 2022

Cited by 4 | Viewed by 2572

Abstract

Law enforcement authorities deal with terrorism in two ways: prevention and legal procedures to establish the offence of forming a terrorist organization. Setting up the offence of a terrorist organization requires proof that the members of the organization acquire distinct roles in the [...] Read more.

Law enforcement authorities deal with terrorism in two ways: prevention and legal procedures to establish the offence of forming a terrorist organization. Setting up the offence of a terrorist organization requires proof that the members of the organization acquire distinct roles in the organization. Until today, this procedure has been based on unreliable, biased or subjective witness statements, resulting in questionable criminal court proceedings. A quantitative, unbiased methodology based on Network Theory is proposed in order to address three research questions: “How can the presence of distinct roles among the members of a terrorist organization be revealed?”, “Is the presence of distinct roles related to terrorist activity?”and “Are there early signs of imminent terrorist activity?”. These questions are addressed using selected global indices from network theory: density, small worldness, centralization, average centrality and standard deviation of centrality. These indices are computed for four real networks of terrorist organizations from four different countries. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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15 pages, 413 KiB

Open AccessArticle

Community Detection Based on Node Influence and Similarity of Nodes

by Yanjie Xu, Tao Ren and Shixiang Sun

Mathematics 2022, 10(6), 970; https://doi.org/10.3390/math10060970 - 18 Mar 2022

Cited by 2 | Viewed by 2477

Abstract

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an [...] Read more.

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an agglomerative community detection algorithm, which is based on node influence and the similarity of nodes. The proposed algorithm consists of three essential steps: identifying the central node based on node influence, selecting a candidate neighbor to expand the community based on the similarity of nodes, and merging the small community based on the similarity of communities. The performance and effectiveness of the proposed algorithm were tested on real and synthetic networks, and they were further evaluated through modularity and NMI anlaysis. The experimental results show that the proposed algorithm is effective in community detection and it is quite comparable to existing classic methods. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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18 pages, 1006 KiB

Open AccessArticle

Multiple Benefit Thresholds Problem in Online Social Networks: An Algorithmic Approach

by Phuong N. H. Pham, Bich-Ngan T. Nguyen, Quy T. N. Co and Václav Snášel

Mathematics 2022, 10(6), 876; https://doi.org/10.3390/math10060876 - 09 Mar 2022

Cited by 1 | Viewed by 1750

Abstract

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit [...] Read more.

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds,

T = {T_{1}, T_{2}, \dots, T_{k}}, T_{i} > 0

, the issue aims to seek the seed sets

S_{1}, S_{2}, \dots, S_{k}

with the lowest possible cost so that the benefit achieved from the influence process is at the very least

T_{1}, T_{2}, \dots, T_{k}

, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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13 pages, 705 KiB

Open AccessArticle

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

by Dongling Yu and Zuguo Yu

Mathematics 2022, 10(5), 801; https://doi.org/10.3390/math10050801 - 02 Mar 2022

Cited by 4 | Viewed by 1812

Abstract

Cancer drivers play an important role in regulating cell growth, cell cycles, and DNA replication. Identifying these cancer drivers provides cancer researchers with indispensable knowledge that has important implications for clinical decision making. Some methods have been recently proposed to identify coding and [...] Read more.

Cancer drivers play an important role in regulating cell growth, cell cycles, and DNA replication. Identifying these cancer drivers provides cancer researchers with indispensable knowledge that has important implications for clinical decision making. Some methods have been recently proposed to identify coding and non-coding cancer drivers through controllability analysis in network and eigenvector centrality based on community detection. However, the performance of these methods is not satisfactory. In this work, we focus on the strategy of selecting a set of critical nodes in cancer-special network as cancer drivers, and propose a novel approach for identifying coding and non-coding drives via a network-based voting mechanism. We name our approach HWVoteRank. Compared with two recent methods to identify cancer drivers, CBNA and NIBNA, and three algorithms for identifying key nodes on BRCA dataset, our method can achieve the best efficiency. By analyzing the results, it is found that our approach has better ability in identifying miRNA cancer drivers. We also applied our approach to identification of drivers of miRNA during Epithelial–Mesenchymal transition and drivers for cancer subtype. Through literature research, we found that those drivers explored by our approach are of biological significance. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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13 pages, 2850 KiB

Open AccessArticle

Network Representation Learning Algorithm Based on Complete Subgraph Folding

by Dongming Chen, Mingshuo Nie, Jiarui Yan, Dongqi Wang and Qianqian Gan

Mathematics 2022, 10(4), 581; https://doi.org/10.3390/math10040581 - 13 Feb 2022

Cited by 2 | Viewed by 1474

Abstract

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph [...] Read more.

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph clustering. Existing algorithms commonly ignore the global topological information of the network in network representation learning, leading to information loss. The complete subgraph in the network commonly has a community structure, or it is the component module of the community structure. We believe that the structure of the community serves as the revealed structure in the topology of the network and preserves global information. In this paper, we propose SF-NRL, a network representation learning algorithm based on complete subgraph folding. The algorithm preserves the global topological information of the original network completely, by finding complete subgraphs in the original network and folding them into the super nodes. We employ the network representation learning algorithm to study the node embeddings on the folded network, and then merge the embeddings of the folded network with those of the original network to obtain the final node embeddings. Experiments performed on four real-world networks prove the effectiveness of the SF-NRL algorithm. The proposed algorithm outperforms the baselines in evaluation metrics on community detection and multi-label classification tasks. The proposed algorithm can effectively generalize the global information of the network and provides excellent classification performance. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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19 pages, 8868 KiB

Open AccessEditor’s ChoiceArticle

Global Value Chains of COVID-19 Materials: A Weighted Directed Network Analysis

by Georgios Angelidis, Charalambos Bratsas, Georgios Makris, Evangelos Ioannidis, Nikos C. Varsakelis and Ioannis E. Antoniou

Mathematics 2021, 9(24), 3202; https://doi.org/10.3390/math9243202 - 11 Dec 2021

Cited by 7 | Viewed by 2603

Abstract

The COVID-19 pandemic caused a boom in demand for personal protective equipment, or so-called “COVID-19 goods”, around the world. We investigate three key sectoral global value chain networks, namely, “chemicals”, “rubber and plastics”, and “textiles”, involved in the production of these goods. First, [...] Read more.

The COVID-19 pandemic caused a boom in demand for personal protective equipment, or so-called “COVID-19 goods”, around the world. We investigate three key sectoral global value chain networks, namely, “chemicals”, “rubber and plastics”, and “textiles”, involved in the production of these goods. First, we identify the countries that export a higher value added share than import, resulting in a “value added surplus”. Then, we assess their value added flow diversification using entropy. Finally, we analyze their egonets in order to identify their key affiliates. The relevant networks were constructed from the World Input-Output Database. The empirical results reveal that the USA had the highest surplus in “chemicals”, Japan in “rubber and plastics”, and China in “textiles”. Concerning value added flows, the USA was highly diversified in “chemicals”, Germany in “rubber and plastics”, and Italy in “textiles”. From the analysis of egonets, we found that the USA was the key supplier in all sectoral networks under consideration. Our work provides meaningful conclusions about trade outperformance due to the fact of surplus, trade flow robustness due to the fact of diversification, and trade partnerships due to the egonets analysis. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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17 pages, 2304 KiB

Open AccessArticle

Influence Maximization Based on Backward Reasoning in Online Social Networks

by Lin Zhang and Kan Li

Mathematics 2021, 9(24), 3189; https://doi.org/10.3390/math9243189 - 10 Dec 2021

Cited by 1 | Viewed by 1797

Abstract

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to [...] Read more.

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-

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nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising. Full article

(This article belongs to the Special Issue Complex Network Modeling: Theory and Applications)

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