Knowledge Graph Enhanced Community Detection and Characterization

Bhatt, Shreyansh; Padhee, Swati; Sheth, Amit P.; Chen, Keke; Shalin, Valerie L.; Minnery, Brandon S.

doi:10.1145/3289600.3291031

Cited by 27 publications

(13 citation statements)

References 27 publications

(32 reference statements)

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“…Bhatt et al [13] proposed a label propagation-based method for detecting the structure of a community based on the context describing it. The method predicts the common context that summarize a potential community's nodes.…”

Section: ) Vertex Centric Clustering Category A: Methods That Apply Vertex Label Propagation Based On Stochastic Processmentioning

confidence: 99%

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Taha

2020

IEEE Access

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Most current survey papers classify community detection methods into broad categories and do not draw clear boundaries between the specific techniques employed by these methods. We survey in this paper all fine-grained community detection categories, the clustering methods that fall under these categories, and the techniques employed by these methods for optimizing each objective function. We provide methodology-based taxonomies that classify static and dynamic community detection methods into hierarchically nested, fine-grained, and specific classes. We classify the methods into the objective function they optimize. Each objective function class is classified into clustering categories. Each category is further classified into clustering methods. Methods are further classified into sub-methods and so on. Thus, the lowest subclass in a hierarchy is a fine-grained and specific method. For each method, we survey the different techniques in literature employed by the method. We empirically and experimentally compare and rank the different methods that fall under each clustering category. We also empirically and experimentally compare and rank the different categories that optimize a same objective function. In summary, the blockbased, top-down divisive-based, random walk-based, and matrix eigenvector-based methods achieved good results. Finally, we provide fitness metrics for each objective function.

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Section: ) Vertex Centric Clustering Category A: Methods That Apply Vertex Label Propagation Based On Stochastic Processmentioning

confidence: 99%

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Taha

2020

IEEE Access

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“…We argue that considering the characteristics of each node as well as each edge is crucial so that we may find nodes in a community having an intense relationship among themselves with similar features. Moreover, as described by Bhatt et al [16], one can get a full meaning of the structures of communities in a complex network as far as both network topology and node attributes are considered in the process of community detection.…”

Section: A Community Identification Methodsmentioning

confidence: 99%

“…The PLANE method employed k-means algorithm to the community detection process out of the embedded nodes. Bhatt et al [16] proposed a context-oriented community detection method by applying a weighted knowledge graph. Bhatt et al employed the Louvain community detection algorithm [18] and introduced a contextual similarity assessment method for describing node pair similarities to apprehend community contexts.…”

Section: A Community Identification Methodsmentioning

confidence: 99%

TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks

et al. 2020

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Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic-aware subnetwork identification is essential to discover potential researchers on particular research topics and provide quality work. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1) How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic.

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“…As a result of our corpus assembly process, we can recover network structure [45] suitable for insider-outsider analysis. Finally, with the caveat of access restricted to public accounts, our corpus approximates a realistic class balance of benign and toxic content.…”

Section: Need For Curated Corporamentioning

confidence: 99%

Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

Sheth¹,

Shalin²,

Kursuncu³

2021

Preprint

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As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper, we define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.

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Knowledge Graph Enhanced Community Detection and Characterization

Cited by 27 publications

References 27 publications

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks

Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

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