Local Motif Clustering on Time-Evolving Graphs

Fu, Dongqi; Zhou, Dawei; He, Jingrui

doi:10.1145/3394486.3403081

Cited by 34 publications

(33 citation statements)

References 12 publications

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“…Our work is also related to recent advances in modeling and generation of graphs (Li et al, 2018a;Jin et al, 2018;Grover et al, 2019;Simonovsky and Komodakis, 2018;Liu et al, 2019;Fu et al, 2020;Dai et al, 2020;You et al, 2018;Liao et al, 2019;Yoo et al, 2020;Shi et al, 2020). We are the first to perform graph generation on event graphs.…”

Section: Related Workmentioning

confidence: 84%

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

Li¹,

Li²,

Wang³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema. To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas. Intrinsic evaluations by schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 23.8% on

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Section: Related Workmentioning

confidence: 84%

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

Li¹,

Li²,

Wang³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Techniques to deal with large networks include sampling [9], network partitioning -namely local clustering [10] and community detection [11], and studying diffusion and influence [12]. GUDIE aims to extracts the relevant context around a node.…”

Section: Related Workmentioning

confidence: 99%

“…Local graph clustering shares similarities to our problem since its goal is to identify a cluster near a given node [10]. A local cluster is a group of nodes around a seed node with high connectivity between local nodes and low connectivity to nodes outside the cluster.…”

Section: Related Workmentioning

confidence: 99%

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GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs

Silva,

Aparício,

Malveiro

et al. 2021

Preprint

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Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often necessary to partition them into meaningful units of analysis. In this work, we propose GUDIE, a message-passing algorithm that extracts relevant context around seed nodes based on user-defined criteria. We design GUDIE for rich, labeled graphs, and expansions consider node and edge attributes. Preliminary results indicate that GUDIE expands to insightful areas while avoiding unimportant connections. The resulting subgraphs contain the relevant context for a seed node and can accelerate and extend analysis capabilities in finance and other critical networks.

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“…Recently, higher-order patterns in networks have attracted much research interest because of their tremendous success in many real-world applications (Milo et al, 2002;Alon, 2007) including discovering data insights (Benson et al, 2016;Paranjape et al, 2017;Benson et al, 2018;Lambiotte et al, 2019;Do et al, 2020) and building scalable computing algorithms (Yin et al, 2017;Paranjape et al, 2017;Fu et al, 2020;Veldt et al, 2020). Previous works on higher-order structure prediction can be generally grouped into two categories, predicting multiple edges/subgraphs in graphs (Lahiri and Berger-Wolf, 2007;Meng et al, 2018;Nassar et al, 2020;Cotta et al, 2020) and predicting hyperedges in hypergraphs Benson et al, 2018;Yadati et al, 2020;Alsentzer et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Neural Predicting Higher-order Patterns in Temporal Networks

Liu¹,

Ma²,

Li³

2021

Preprint

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Dynamic systems that consist of a set of interacting elements can be abstracted as temporal networks. Recently, higher-order patterns that involve multiple interacting nodes have been found crucial to indicate domain-specific laws of different temporal networks. This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms. Here, we propose the first model, named HIT, for higher-order pattern prediction in temporal hypergraphs. Particularly, we focus on predicting three types of common but important interaction patterns involving three interacting elements in temporal networks, which could be extended to even higher-order patterns. HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet. HIT could achieve significant improvement (averaged 20% AUC gain to identify the interaction type, uniformly more accurate time estimation) compared to both heuristic and other neural-network-based baselines on 5 real-world large temporal hypergraphs. Moreover, HIT provides a certain degree of interpretability by identifying the most discriminatory structural features on the temporal hypergraphs for predicting different higher-order patterns.

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Local Motif Clustering on Time-Evolving Graphs

Cited by 34 publications

References 12 publications

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs

Neural Predicting Higher-order Patterns in Temporal Networks

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