Distance-generalized Core Decomposition

Bonchi, Francesco; Khan, Arijit; Severini, Lorenzo

doi:10.1145/3299869.3324962

Cited by 62 publications

(57 citation statements)

References 72 publications

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“…First, the most commonly one is the minimum degree [11]- [14], which demands that degree of each node is at least within the subgraph. Meanwhile, the minimum degree is also applied to -core [15], [16], and [17]- [19] propose to use -core model to search cohesive subgraphs in attributed graphs. Then, some works study the model oftruss [11], [20]- [23], and it requires one edge within the subgraph is contained in triangles.…”

Section: Related Workmentioning

confidence: 99%

Effective and Efficient Steiner Maximum Path-Connected Subgraph Search in Large Social Internet of Things

Fan

Sun

et al. 2021

IEEE Access

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Social Internet of Things (SIoT) is extended to integrate social networks in the Internet of Things (IoT). SIoT enriches IoT, and thus resource (or service) discovery and consolidation in SIoT becomes an important and challenging problem. In this paper, we represent the SIoT as the heterogeneous information networks (HINs) with multi-typed entities and relations, and resolve this problem from the perspective of cohesive subgraph search. Specifically, given a query node , we discover a cohesive subgraph containing from a HIN, where all nodes are of the same type as and have dense relationships. Yet existing solutions cannot be applied to various HINs, and the result subgraph is not accurate and close enough due to ignoring the connectivity among nodes within the subgraph. To this end, 1) we extend the connectivity with novel meta-path-based edge-disjoint paths to HINs, and propose the -path connected component ( -PCC) to measure the cohesiveness of subgraph in HINs; 2) we model the densest connected subgraph containing as the -PCC with the maximum connectivity including , called the Steiner Maximum Path-Connected Subgraph (SMPCS); 3) we develop efficient algorithms based on an index tree for searching the SMPCS. Extensive experiments on four real HINs are conducted to demonstrate the effectiveness and efficiency of our proposed approaches. INDEX TERMSCohesive subgraph search, heterogeneous information network (HIN), meta-path, Social Internet of Things (SIoT).

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Section: Related Workmentioning

confidence: 99%

Effective and Efficient Steiner Maximum Path-Connected Subgraph Search in Large Social Internet of Things

Fan

Sun

et al. 2021

IEEE Access

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“…According to the concepts of k-core [17], [18], [21] and skyline [19], [25], the skyline community is defined in the multi-valued graph. Therefore, a skyline community in the multi-valued graph G should be cohesive and not be dominated by other communities.…”

Section: Problem Statementmentioning

confidence: 99%

Optimized Algorithm for Skyline Community Discovery in Multi-Valued Networks

et al. 2021

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The community discovery problem aims to find the close subgraphs from the traditional network. The traditional network with multiple numerical attributes of nodes is called the multi-valued network. The Skyline Community is the largest connected k-core that is not dominated in a multi-valued network, which can be used to solve multi-objective decision-making problems. In this paper, we focus on the skyline community discovery problem. Firstly, we propose a combined index that includes the degreeneighbor (D-N) index and inverted index to manage the multi-valued network. The index can filter the nodes whose degrees do not satisfy k. Simultaneously, it can accelerate the judgment of the neighbor relationship between the nodes and improve the query efficiency. Then, we propose the SCDCI (Skyline Community Discovery Algorithm Based on Combined Index Structure) algorithm to compute the skyline community. The algorithm scans the nodes in turn from the inverted indexes, avoiding lots of redundant calculations. The nodes are scanned in descending order by their attributes, therefore, lots of nodes that cannot form a skyline community do not be processed. Finally, the effectiveness of SCDCI algorithm is verified through experiments. Compared with the existing algorithms, the efficiency of our algorithm is improved a lot.

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“…Dense subgraph extraction in social networks. The dense subgraph extraction problem aims at extracting densely connected communities in social networks [4,6,8,13,15,16,22,28,32,36]. For example, Fang et al consider the keywords and social tightness to select nodes from the social network to form a -core [8].…”

Section: Related Workmentioning

confidence: 99%

WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks

Hsu

Shen

Chang

2020

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

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The data of egocentric networks (ego networks) are very important for evaluating and validating the algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and only a small number of users would agree to contribute their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users' willingness to contribute their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we make our first attempt to address this issue by proposing a new research problem, named Willingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo), to identify a set of ego networks from the OSN such that the willingness of the users to contribute their data is maximized. We prove that WMEgo is NP-hard and propose a 1 2 (1 − 1)approximation algorithm, named Ego Network Identification with Maximum Willingness (EIMW). We conduct an evaluation study with 672 volunteers to validate the proposed WMEgo and EIMW, and perform extensive experiments on multiple real datasets to demonstrate the effectiveness and efficiency of our approach.

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Distance-generalized Core Decomposition

Cited by 62 publications

References 72 publications

Effective and Efficient Steiner Maximum Path-Connected Subgraph Search in Large Social Internet of Things

Effective and Efficient Steiner Maximum Path-Connected Subgraph Search in Large Social Internet of Things

Optimized Algorithm for Skyline Community Discovery in Multi-Valued Networks

WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks

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