Evolution of Real-World Hypergraphs: Patterns and Models without Oracles

Kook, Yunbum; Ko, Jihoon; Shin, Kijung

doi:10.1109/icdm50108.2020.00036

Cited by 28 publications

(28 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A large volume of past work has been done on the evolution of global dyadic graphs [14]- [16]. The evolution of dynamic systems that model higher-order interactions using hypergraphs has also been previously investigated [4], [5], [9], [11]. [4] studies the temporal evolution of global hypergraph datasets in the context of simplicial closures and link prediction, and also looks at predicting system domain using higher-order ego-networks.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, we do not use the projected graph in our paper to analyze higher-order networks. [11] examines temporal properties of global hypergraphs in order to realistically generate hypergraphs. In contrast, our paper instead focuses on modelling local hypergraph structure.…”

Section: Related Workmentioning

confidence: 99%

“…Interactions involving multiple objects simultaneously are ubiquitous across many domains: academic papers tend to have multiple co-authors, emails are sent to multiple recipients, and bills in congress are co-sponsored by multiple members. A growing body of research has been dedicated to understanding the structure of these higher-order interactions [4]- [6], [9], [11], seeking to explore these interactions by analyzing the macroscopic trends of the interactions within these systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, very little is known about temporal hypergraphs, which can be viewed as an ordered, timestamped sequence of hypergraphs. Kook et al [11] have recently considered aggregate patterns found in real-world temporal hypergraphs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hypergraph Ego-networks and Their Temporal Evolution

Comrie¹,

Kleinberg²

2021

Preprint

View full text Add to dashboard Cite

Interactions involving multiple objects simultaneously are ubiquitous across many domains. The systems these interactions inhabit can be modelled using hypergraphs, a generalization of traditional graphs in which each edge can connect any number of nodes. Analyzing the global and static properties of these hypergraphs has led to a plethora of novel findings regarding how these modelled systems are structured. However, less is known about the localized structure of these systems and how they evolve over time.In this paper, we propose the study of hypergraph egonetworks, a structure that can be used to model higher-order interactions involving a single node. We also propose the temporal reconstruction of hypergraph ego-networks as a benchmark problem for models that aim to predict the local temporal structure of hypergraphs. By combining a deep learning binary classifier with a hill-climbing algorithm, we will present a model for reconstructing hypergraph ego-networks by incorporating structural patterns found across multiple domains.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hypergraph Ego-networks and Their Temporal Evolution

Comrie¹,

Kleinberg²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, several empirical studies have revealed structural and temporal properties of real-world hypergraphs. Pervasive structural patterns include (a) heavy-tailed distributions of degrees, edge sizes, and intersection sizes [1]; (b) giant connected components [2], and small diameters [2]; and (c) substantial overlaps of hyperedges with homophily [3]. Temporal properties observed commonly in various time-evolving hypergraphs include (a) significant overlaps between temporally adjacent hyperedges [4]; and (b) diminishing overlaps, densification, and shrinking diameters [1].…”

Section: Introductionmentioning

confidence: 99%

THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting

Lee¹,

Shin²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Group interactions arise in our daily lives (email communications, on-demand ride sharing, comment interactions on online communities, to name a few), and they together form hypergraphs that evolve over time. Given such temporal hypergraphs, how can we describe their underlying design principles? If their sizes and time spans are considerably different, how can we compare their structural and temporal characteristics?In this work, we define 96 temporal hypergraph motifs (THmotifs), and propose the relative occurrences of their instances as an answer to the above questions. TH-motifs categorize the relational and temporal dynamics among three connected hyperedges that appear within a short time. For scalable analysis, we develop THYME + , a fast and exact algorithm for counting the instances of TH-motifs in massive hypergraphs, and show that THYME + is at most 2 , 163 × faster while requiring less space than baseline. Using it, we investigate 11 real-world temporal hypergraphs from various domains. We demonstrate that THmotifs provide important information useful for downstream tasks and reveal interesting patterns, including the striking similarity between temporal hypergraphs from the same domain.

show abstract

Growth patterns and models of real-world hypergraphs

Kook

Shin

2022

Knowl Inf Syst

View full text Add to dashboard Cite

Evolution of Real-World Hypergraphs: Patterns and Models without Oracles

Cited by 28 publications

References 41 publications

Hypergraph Ego-networks and Their Temporal Evolution

Hypergraph Ego-networks and Their Temporal Evolution

THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting

Growth patterns and models of real-world hypergraphs

Contact Info

Product

Resources

About