Butterfly-core community search over labeled graphs

Dong, Zheng; Huang, Xin; Yuan, Guorui; Zhu, Hengshu; Xiong, Hui

doi:10.14778/3476249.3476258

Cited by 24 publications

(4 citation statements)

References 42 publications

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“…Since then, various community models have been proposed based on different pre-defined dense subgraphs [13], including 𝑘-core [40,41], 𝑘-truss [2,23], quasi-clique [11], 𝑘-plex [48], and densest subgraph [53]. Recently, community search has also been explored in directed [14,15], weighted [55], geo-social [21,56], multilayer [3,4,24], multi-valued [27], and labeled [12] graphs. Inspired by the success of Graph Neural Networks (GNNs), recently, several GNN-based approaches have been proposed for community search, such as [3,18,25].…”

Section: Related Workmentioning

confidence: 99%

CS-TGN: Community Search via Temporal Graph Neural Networks

Hashemi

Behrouz

Hajidehi

2023

Companion Proceedings of the ACM Web Conference 2023

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Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks. Given any query nodes, Community Search aims to find a densely connected subgraph containing query nodes. However, existing community search approaches in temporal networks have two main limitations:(1) they adopt pre-defined subgraph patterns to model communities, which cannot find communities that do not conform to these patterns in real-world networks, and (2) they only use the aggregation of disjoint structural information to measure quality, missing the dynamic of connections and temporal properties. In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ground-truth communities in a data-driven manner. CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with contextual attention to learn the dynamics of interactions and update node embeddings over time. We demonstrate how this model can be used for interactive community search in an online setting, allowing users to evaluate the found communities and provide feedback. Experiments on real-world temporal graphs with ground-truth communities validate the superior quality of the solutions obtained and the efficiency of our model in both temporal and interactive static settings. CCS CONCEPTS• Computing methodologies → Neural networks; • Mathematics of computing → Graph algorithms.

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Section: Related Workmentioning

confidence: 99%

CS-TGN: Community Search via Temporal Graph Neural Networks

Hashemi

Behrouz

Hajidehi

2023

Companion Proceedings of the ACM Web Conference 2023

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“…Since then, various community models have been presented based on different pre-defined cohesive graph patterns [2,7,19,20,35,38]. More recently, CS has also been investigated for more complex graphs, such as directed [10,11], geo-social [16,41], temporal [29], multi-valued [28], weighted [40], and labeled [8] graphs. To address the structural inflexibility of these models, recently, graph neural network-based approaches have been introduced [15,22].…”

Section: Motivationsmentioning

confidence: 99%

CS-MLGCN: Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks

Behrouz

Hashemi

2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in many applications such as biological, social, and transportation networks, interactions between objects span multiple aspects, yielding networks with multiple views, called multiplex networks. Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks. In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. CS-MLGCN first combines the local query-dependent structure and global graph embedding in each type of proximity and then uses an attention mechanism to incorporate information on different types of relations. Experiments on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of our model. CCS CONCEPTS• Computing methodologies → Neural networks; • Mathematics of computing → Graph algorithms.

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“…The SVGD problem is distinct from conventional friend recommendation and personalized recommendation approaches (Cheng et al 2019;Chen et al 2017;Zhao et al 2016;Bagci and Karagoz 2016;Lin et al 2017), as well as cohesive group extraction in social networks (Lu et al 2022;Al-Baghdadi and Lian 2020;Ma et al 2022;Sanei-Mehri et al 2021;Dong et al 2021;Yang et al 2021Yang et al , 2012a. Conventional friend recommendation focuses on suggesting potential friends based on preferences, without considering the impact of other users' configurations in a VR setting.…”

Section: Introductionmentioning

confidence: 99%

Social-Aware Group Display Configuration in VR Conference

Hsu,

Shen,

Yuan

et al. 2024

AAAI

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Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature -- displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality.

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Butterfly-core community search over labeled graphs

Cited by 24 publications

References 42 publications

CS-TGN: Community Search via Temporal Graph Neural Networks

CS-TGN: Community Search via Temporal Graph Neural Networks

CS-MLGCN: Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks

Social-Aware Group Display Configuration in VR Conference

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