Hyperlink Prediction in Hypernetworks Using Latent Social Features

Xu, Ye; Rockmore, Daniel N.; Kleinbaum, Adam M.

doi:10.1007/978-3-642-40897-7_22

Cited by 27 publications

(26 citation statements)

References 32 publications

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“…A rigorous study of HLP began with the near-seminal works [1,23] on hypergraph Laplacian and spectral clustering methods. Xu et al [19] explore the latent representation of hyperlinks obtained via those of nodes and a novel entropybased approach to combine them. More recently, Zhang et al [21,22] proposed CMM, which is the current state-of-the-art for HLP.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Although the problem of hyperlink prediction (HLP) has not been explored much, we have enough literature on the topic [3,19,21,22] (and much more on its graph-variant, viz., link prediction (LP) [11,14,15,18]) to vouch for its importance. When posed as a supervised learning problem, where "presence of hyperedge" and "absence of hyperedge" are the positive and negative classes respectively, HLP suffers from extreme class imbalance (ECI), with positive class being the minority one.…”

Section: Introductionmentioning

confidence: 99%

“…1b. Of the four, UNS and SNS are both motivated by random NS in LP [2], and have already been used in the literature to predict new recipes [21] and new email interactions [19]. We derive MNS from a motif-based representative subgraph sampling [4,9] and CNS is our attempt towards developing a 1-hop based equivalent of NS [13] to HLP.…”

Section: Introductionmentioning

confidence: 99%

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Negative Sampling for Hyperlink Prediction in Networks

Patil

Sharma

Murty

2020

Advances in Knowledge Discovery and Data Mining

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While graphs capture pairwise relations between entities, hypergraphs dealPrasanna with higher-order ones, thereby ensuring losslessness. However, in hyperlink (i.e., higher-order link) prediction, where hyperlinks and non-hyperlinks are treated as "positive" and "negative" classes respectively, hypergraphs suffer from the problem of extreme class imbalance. Given this context, "negative sampling"-under-sampling the negative class of non-hyperlinks-becomes mandatory for performing hyperlink prediction. No prior work on hyperlink prediction deals with this problem. In this work, which is the first of its kind, we deal with this problem in the context of hyperlink prediction. More specifically, we leverage graph sampling techniques for sampling non-hyperlinks in hyperlink prediction. Our analysis clearly establishes the effect of random sampling, which is the norm in both link-as well as hyperlink-prediction. Further, we formalize the notion of "hardness" of non-hyperlinks via a measure of density, and analyze its distribution over various negative sampling techniques. We experiment with some real-world hypergraph datasets and provide both qualitative and quantitative results on the effects of negative sampling. We also establish its importance in evaluating hyperlink prediction algorithms. Olivia Flora (a) The SWC hypergraph CNS MNS SNS (b) Edge density and size distribution of non-hyperlinks

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Section: State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Negative Sampling for Hyperlink Prediction in Networks

Patil

Sharma

Murty

2020

Advances in Knowledge Discovery and Data Mining

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“…Figure 2 (Middle) shows an example of such a transition from an open triangle to a closed one. Recently, several works have proposed modeling higher-order interactions as hyperedges in a hypergraph (Xu et al, 2013;Zhang et al, 2018;Yoon et al, 2020;Patil et al, 2020). Given a hyperedge h t at time t, the inference task is to predict the future arrival of a new hyperedge h t at time t > t, which covers a larger set of vertices than h t and contains all the vertices in h t .…”

Section: Introductionmentioning

confidence: 99%

Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach

Kaul,

Imaizumi

2021

Preprint

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Dynamic graphs are rife with higher-order interactions, such as co-authorship relationships and protein-protein interactions in biological networks, that naturally arise between more than two nodes at once. In spite of the ubiquitous presence of such higher-order interactions, limited attention has been paid to the higher-order counterpart of the popular pairwise link prediction problem. Existing higher-order structure prediction methods are mostly based on heuristic feature extraction procedures, which work well in practice but lack theoretical guarantees. Such heuristics are primarily focused on predicting links in a static snapshot of the graph. Moreover, these heuristic-based methods fail to effectively utilize and benefit from the knowledge of latent substructures already present within the higher-order structures. In this paper, we overcome these obstacles by capturing higher-order interactions succinctly as simplices, model their neighborhood by face-vectors, and develop a nonparametric kernel estimator for simplices that views the evolving graph from the perspective of a time process (i.e., a sequence of graph snapshots). Our method substantially outperforms several baseline higher-order prediction methods. As a theoretical achievement, we prove the consistency and asymptotic normality in terms of the Wasserstein distance of our estimator using Stein's method.

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“…Recently, the concept of hypernetworks 6 has attracted much attention in the scientific community. Hyperlink prediction in hypernetworks using latent social features was studied 7 . Kim et al .…”

mentioning

confidence: 99%

The Evolution of Hyperedge Cardinalities and Bose-Einstein Condensation in Hypernetworks

Guo

Suo

Shen

et al. 2016

Sci Rep

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To depict the complex relationship among nodes and the evolving process of a complex system, a Bose-Einstein hypernetwork is proposed in this paper. Based on two basic evolutionary mechanisms, growth and preference jumping, the distribution of hyperedge cardinalities is studied. The Poisson process theory is used to describe the arrival process of new node batches. And, by using the Poisson process theory and a continuity technique, the hypernetwork is analyzed and the characteristic equation of hyperedge cardinalities is obtained. Additionally, an analytical expression for the stationary average hyperedge cardinality distribution is derived by employing the characteristic equation, from which Bose-Einstein condensation in the hypernetwork is obtained. The theoretical analyses in this paper agree with the conducted numerical simulations. This is the first study on the hyperedge cardinality in hypernetworks, where Bose-Einstein condensation can be regarded as a special case of hypernetworks. Moreover, a condensation degree is also discussed with which Bose-Einstein condensation can be classified.

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Hyperlink Prediction in Hypernetworks Using Latent Social Features

Cited by 27 publications

References 32 publications

Negative Sampling for Hyperlink Prediction in Networks

Negative Sampling for Hyperlink Prediction in Networks

Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach

The Evolution of Hyperedge Cardinalities and Bose-Einstein Condensation in Hypernetworks

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