Community detection in node-attributed social networks: How structure-attributes correlation affects clustering quality

Chunaev, Petr; Gradov, Timofey; Bochenina, Klavdiya

doi:10.1016/j.procs.2020.11.037

Cited by 9 publications

(11 citation statements)

References 13 publications

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“…Analyzing the algorithm performances as functions of the whole range of structure and attribute parameter values allowed us to have a broad vision of how algorithms perform. Nevertheless, as well remarked by several discussions (Fortunato and Hric 2016;Chunaev 2020;Chunaev et al 2020), a strong rationale behind many of LCD approaches is often assumed by the researchers: the algorithms can exploit nodes' attributes in the CD task because homophily strongly contributes to community formation. In other words, since node similarities match with the connections they made, it is useful to consider such similarities while grouping closer nodes.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, driven by the homophily principle (McPherson et al 2001), node attributes are often used to improve CD-or, at least, redefine it w.r.t. external aspects (Chunaev et al 2020)-by leveraging both topological and label-homogeneous clustering criteria. The node-attributed network encodes information about the node's properties/qualities, in form of attributes, accordingly to the general purposes of feature-rich networks (Interdonato et al 2019), where the goal is to merge the graph topology together with other possibly meaningful external information.…”

Section: Introductionmentioning

confidence: 99%

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X-Mark: a benchmark for node-attributed community discovery algorithms

Citraro

Rossetti

2021

Soc. Netw. Anal. Min.

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Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

X-Mark: a benchmark for node-attributed community discovery algorithms

Citraro

Rossetti

2021

Soc. Netw. Anal. Min.

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“…While topology is important, other factors, such as correlation of the nodes to a target variable, have no effect on clustering outcomes. Attributed network clustering techniques have previously been proposed to address this shortcoming (Chunaev et al, 2020 ). Early fusion methods, such as that proposed by Bhatt et al, merge node attributes and graph structure together before applying a clustering algorithm (Bhatt et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Abdel-Hafiz¹,

Najafi²,

Helmi³

et al. 2022

Front. Big Data

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Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

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“…However, attributes might be inconsistent with topology in the perspective of community structure. Therefore, the attributes of nodes should be processed carefully to improve community detection accuracy, e.g., setting a balance value between topology and attributes to fit the different structureattributes correlation [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Social Networks Considering Social Behaviors

Wang¹,

Jin

et al. 2022

IEEE Access

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The study of community detection in networks has drawn great attention in recent years. To find communities and to understand community semantics, both network topology and network content are utilized. Unfortunately, none of them can explain the driving factors of generating community structure with semantics, which is significant for understanding the mechanisms of community generation. Our observations on a large number of networks show that specific user social behaviors are underlying factors for the generation of community structure. We exploit four types of social behaviors that widely exist in networks, i.e., reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics. We investigate their impacts on the formation process of links and content in networks, during which communities with topics form. Our analysis shows that they are highly related to community structure. Consequently, a generative community detection model SBCD (social behavior-based community detection) is proposed by combining network topology and content, in which the above social behaviors play a core role. The model is evaluated on two real datasets. The experimental results show that SBCD outperforms state-of-the-art baselines. Finally, a case study illustrates several significant observations with respect to the proposed social behaviors.

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Community detection in node-attributed social networks: How structure-attributes correlation affects clustering quality

Cited by 9 publications

References 13 publications

X-Mark: a benchmark for node-attributed community discovery algorithms

X-Mark: a benchmark for node-attributed community discovery algorithms

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Community Detection in Social Networks Considering Social Behaviors

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