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Algorithms
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Graph Embedding Applications
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Graph Embedding Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 12366

Special Issue Editor

Dr. Xiao Huang

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
Interests: graph neural networks; knowledge graphs; network anomaly detection; recommender systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many networked systems have involved machine learning algorithms to perform various prediction tasks, such as social media, academic networks, biochemical systems, molecular networks, and traffic networks. To handle these complex networks, an effective way is to embed them into low-dimensional representations and conduct downstream applications based on the learned representations. Researchers from related disciplines are developing graph-embedding-based algorithms for different real-world networks and applications.

If you are exploring graph-based algorithms to handle real-world problems, we would strongly encourage you to submit the latest research to this Special Issue, “Graph Embedding Applications”. We are looking for novel algorithms, theories, and applications related to graph embedding. Potential topics include but are not limited to efficient algorithms to accelerate the embedding in network analysis, effective graph-based algorithms tailored to practical issues, theoretical analysis of and insights into graph embedding, and algorithms that bridge the gap between graph embedding and applications in various domains, such as healthcare, commerce, biochemistry, and transportation.

Dr. Xiao Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

20 pages, 3702 KiB  
Article
SumMER: Structural Summarization for RDF/S KGs
by Georgia Eirini Trouli, Alexandros Pappas, Georgia Troullinou, Lefteris Koumakis, Nikos Papadakis and Haridimos Kondylakis
Algorithms 2023, 16(1), 18; https://doi.org/10.3390/a16010018 - 27 Dec 2022
Cited by 8 | Viewed by 1626
Abstract
Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. [...] Read more.
Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. Semantic summaries have been proposed as a key technology enabling the quick understanding and exploration of large knowledge graphs. Among the methods proposed for generating summaries, structural methods exploit primarily the structure of the graph in order to generate the result summaries. Approaches in the area focus on identifying the most important nodes and usually employ a single centrality measure, capturing a specific perspective on the notion of a node’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ importance, leading to better summaries. In this paper, we present SumMER, the first structural summarization technique exploiting machine learning techniques for RDF/S KGs. SumMER explores eight centrality measures and then exploits machine learning techniques for optimally selecting the most important nodes. Then those nodes are linked formulating a subgraph out of the original graph. We experimentally show that combining centrality measures with machine learning effectively increases the quality of the generated summaries. Full article
(This article belongs to the Special Issue Graph Embedding Applications)
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19 pages, 593 KiB  
Article
Fair Benchmark for Unsupervised Node Representation Learning
by Zhihao Guo, Shengyuan Chen, Xiao Huang, Zhiqiang Qian, Chunsing Yu, Yan Xu and Fang Ding
Algorithms 2022, 15(10), 379; https://doi.org/10.3390/a15100379 - 17 Oct 2022
Viewed by 1461
Abstract
Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all actionable patterns in topological structures and side [...] Read more.
Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all actionable patterns in topological structures and side information can be preserved. The widespread availability of networked data, e.g., social media, biological networks, and traffic networks, along with plentiful applications, facilitate the development of NRL. However, it has become challenging for researchers and practitioners to track the state-of-the-art NRL algorithms, given that they were evaluated using different experimental settings and datasets. To this end, in this paper, we focus on unsupervised NRL and propose a fair and comprehensive evaluation framework to systematically evaluate state-of-the-art unsupervised NRL algorithms. We comprehensively evaluate each algorithm by applying it to three evaluation tasks, i.e., classification fine tuned via a validation set, link prediction fine-tuned in the first run, and classification fine tuned via link prediction. In each task and each dataset, all NRL algorithms were fine-tuned using a random search within a fixed amount of time. Based on the results for three tasks and eight datasets, we evaluate and rank thirteen unsupervised NRL algorithms. Full article
(This article belongs to the Special Issue Graph Embedding Applications)
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19 pages, 604 KiB  
Article
On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study
by Alessio Martino, Luca Baldini and Antonello Rizzi
Algorithms 2022, 15(5), 148; https://doi.org/10.3390/a15050148 - 27 Apr 2022
Cited by 6 | Viewed by 2560
Abstract
Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting [...] Read more.
Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules. Full article
(This article belongs to the Special Issue Graph Embedding Applications)
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17 pages, 976 KiB  
Article
Predicting Dynamic User–Item Interaction with Meta-Path Guided Recursive RNN
by Yi Liu, Chengyu Yin, Jingwei Li, Fang Wang and Senzhang Wang
Algorithms 2022, 15(3), 80; https://doi.org/10.3390/a15030080 - 28 Feb 2022
Cited by 1 | Viewed by 2702
Abstract
Accurately predicting user–item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse user–item interaction data without considering their semantic correlations [...] Read more.
Accurately predicting user–item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse user–item interaction data without considering their semantic correlations and the structural information hidden in the data. Another limitation is that existing approaches usually embed the users and items into the different embedding spaces in a static way, but ignore the dynamic characteristics of both users and items. In this paper, we propose to learn the dynamic embedding vector trajectories rather than the static embedding vectors for users and items simultaneously. A Metapath-guided Recursive RNN based Shift embedding method named MRRNN-S is proposed to learn the continuously evolving embeddings of users and items for more accurately predicting their future interactions. The proposed MRRNN-S is extended from our previous model RRNN-S which was proposed in the earlier work. Comparedwith RRNN-S, we add the word2vec module and the skip-gram-based meta-path module to better capture the rich auxiliary information from the user–item interaction data. Specifically, we first regard the interaction data of each user with items as sentence data to model their semantic and sequential information and construct the user–item interaction graph. Then we sample the instances of meta-paths to capture the heterogeneity and structural information from the user–item interaction graph. A recursive RNN is proposed to iteratively and mutually learn the dynamic user and item embeddings in the same latent space based on their historical interactions. Next, a shift embedding module is proposed to predict the future user embeddings. To predict which item a user will interact with, we output the item embedding instead of the pairwise interaction probability between users and items, which is much more efficient. Through extensive experiments on three real-world datasets, we demonstrate that MRRNN-S achieves superior performance by extensive comparison with state-of-the-art baseline models. Full article
(This article belongs to the Special Issue Graph Embedding Applications)
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25 pages, 677 KiB  
Article
Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
by Maximilian Hoffmann and Ralph Bergmann
Algorithms 2022, 15(2), 27; https://doi.org/10.3390/a15020027 - 18 Jan 2022
Cited by 7 | Viewed by 3049
Abstract
Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle [...] Read more.
Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure. Full article
(This article belongs to the Special Issue Graph Embedding Applications)
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