Link prediction in social networks based on hypergraph

Li, Dong; Xu, Zhiming; Li, Sheng; Sun, Xin

doi:10.1145/2487788.2487802

Cited by 57 publications

(30 citation statements)

References 4 publications

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“…Similar to the work here, given the use of NEs as features in the network, is a method of extracting names from websites to mine communities of people [27]. Some work on layered social networks implicitly adopts the hypergraph representation [25,26,44], though most social network analysis still relies on single-mode, unipartite graphs. What's more, most research using hypergraphs is framed as n-partite graph analysis, typically with a small number of layers [4,24].…”

Section: Background and Related Workmentioning

confidence: 98%

Proposing Ties in a Dense Hypergraph of Academics

Gerow

Lou

Duede

et al. 2015

Lecture Notes in Computer Science

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Abstract. Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships.

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Section: Background and Related Workmentioning

confidence: 98%

Proposing Ties in a Dense Hypergraph of Academics

Gerow

Lou

Duede

et al. 2015

Lecture Notes in Computer Science

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“…Link prediction on hypergraph (hyperlink prediction) has been especially popular for social networks to predict higher-order links such as a user releases a tweet containing a hashtag [21] and to predict metadata information such as tags, groups, labels, users for entities (images from Flickr) [1]. Techniques for hyperlink prediction on social networks include ranking for link proximity information [21], matrix completion on the (incomplete) incidence matrix of the hypergraph [1,28], hyperpath-based method [17], and Laplacian tensor methods for context-aware recommendation [39]. These methods are restricted to uniform hypergraphs in which each hyperlink contains the same number of vertices.…”

Section: Related Workmentioning

confidence: 99%

NHP: Neural Hypergraph Link Prediction

Yadati

Ray

Nimishakavi

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Link prediction in simple graphs is a fundamental problem in which new links between vertices are predicted based on the observed structure of the graph. However, in many real-world applications, there is need to model relationships among vertices which go beyond pairwise associations. For example, in a chemical reaction, relationship among the reactants and products is inherently higherorder. Additionally, there is need to represent the direction from reactants to products. Hypergraphs provide a natural way to represent such complex higher-order relationships. Graph Convolutional Networks (GCN) have recently emerged as a powerful deep learning-based approach for link prediction over simple graphs. However, their suitability for link prediction in hypergraphs is underexplored-we fill this gap in this paper and propose Neural Hyperlink Predictor (NHP). NHP adapts GCNs for link prediction in hypergraphs. We propose two variants of NHP-NHP-U and NHP-D-for link prediction over undirected and directed hypergraphs, respectively. To the best of our knowledge, NHP-D is the first ever method for link prediction over directed hypergraphs. An important feature of NHP is that it can also be used for hyperlinks in which dissimilar vertices interact (e.g. acids reacting with bases). Another attractive feature of NHP is that it can be used to predict unseen hyperlinks at test time (inductive hyperlink prediction). Through extensive experiments on multiple real-world datasets, we show NHP's effectiveness. CCS CONCEPTS • Computing methodologies → Neural networks; Unsupervised learning.

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“…In this section, we present some essential terms of hypergraphs. Formally, let G(V, E, w) denote a hypergraph [24], where V denotes a finite set of nodes V, E denotes the set of hyperedges e, and w is a weight function defined as w : E → R.…”

Section: Theory Of the Hypergraph Concept And Definitionsmentioning

confidence: 99%

Hypergraph Application on Business Process Performance

Bouafia

Molnár

2021

Information

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The modeling of the graphical representation of business processes (BP) or workflows in enterprise information systems (IS) is often to represent various activities, entities, relations, functions, and communicate between them in an enterprise to achieve the major goal of operational support. In this work, we decided to use graph representation approaches, especially hypergraphs to depict the complex relationships that exist among the artifacts and constituents of BP for more efficient and accurate manipulations. We used bipartite and further hypergraph formats for storing and curating data. We have investigated the various descriptive languages and representation models of BP as process modeling, workflow and process integration, and object-oriented (OO) languages. We have carried out experiments using different approach combinations, but for observing quiltedrepresentation, we focused on the main consistencies of “DBP”. As the final approach, we used the “DBP” stream and data schemes that are defined by us to proceed with using pure Python for manually generating data and external Python libraries to store, curate, and visualize “DBP”.

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Link prediction in social networks based on hypergraph

Cited by 57 publications

References 4 publications

Proposing Ties in a Dense Hypergraph of Academics

Proposing Ties in a Dense Hypergraph of Academics

NHP: Neural Hypergraph Link Prediction

Hypergraph Application on Business Process Performance

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