From community detection to community profiling

Cai, Hongyun; Zheng, Vincent W.; Zhu, Fanwei; Chang, Kevin Chen-Chuan; Huang, Zi

doi:10.14778/3067421.3067430

Cited by 38 publications

(40 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And indeed, some of them output the most characteristic attributes and/or attribute values of the detected communities (e.g. (Yang, McAuley, et al 2013), or (Cai et al 2017) from the previous paragraph). However, there are two important limitations.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Encyclopedia of Social Network Analysis and Mining

Alhajj¹,

Rokne²

2016

View full text Add to dashboard Cite

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry.

show abstract

Section: Hybrid Approachesmentioning

confidence: 99%

Encyclopedia of Social Network Analysis and Mining

Alhajj¹,

Rokne²

2016

View full text Add to dashboard Cite

show abstract

“…G ENERATING the subgraphs with similar vertices finds important applications in numerous domains: (i) In recommender systems [1] [2], the members in a subgraph can enrich the history of other cold-start members [3][4] [5]. (ii) In community detection, the subgraphs can identify groups of correlated users [6] [7]. (iii) In propagation networks [6] [8], the group-based immunization policies [9] [10] can better control the burst of contagions (i.e.…”

Section: Introductionmentioning

confidence: 99%

SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding

Najafipour

Hosseini

Hua

et al. 2022

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.

show abstract

“…Moreover, social networks are characterized by actors that divide up naturally into groups called "communities". Conventionally, a community is defined as a group of users who interact with each other more frequently than with those outside the group, and that share similar topics of interest [1], [2], [3]. Since social networks are usually modeled by a graph such as nodes represent social actors and edges represent the relationships, a community is defined as a set of nodes that are densely connected among themselves and sparsely connected to the rest of nodes.…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Dynamic Social Networks: A Multi-Agent Aya based on Electric Field

Abdulkreem¹,

Zardi²,

Karamti³

2019

ijacsa

View full text Add to dashboard Cite

In recent years, several approaches have been proposed in order to detect communities in social networks. Most of them suffer from the recurrent problems: no detection of overlapping communities, exponential running time, no detection of all possible communities transformations, don't consider the properties of social members, inability to deal with large scale networks, etc. Multi-agent systems are very suitable for modeling the phenomena in which various autonomous entities in interactions able to evolve in a dynamic environment. Considering the advantages of multi-agent simulations for social networks, in the present study, an incremental multi-agent system based on electric field is proposed. In this approach, a group of autonomous agents work together to discover the dynamic communities. Indeed, an agent is associated to each detected community. To update its community according to the dynamic of its members, each agent creates an electric field around it. It applies an attractive force to add very connected and similar members and neighboring communities. In the same time, it applies a repulsive force to reject some members and to get away from other communities. These forces are based on the structural and attributes similarity. To study the performance of this approach, set of different experiments is performed. The obtained results show the efficiency of the proposed model that was able to overcome all mentioned problems.

show abstract

From community detection to community profiling

Cited by 38 publications

References 40 publications

Encyclopedia of Social Network Analysis and Mining

Encyclopedia of Social Network Analysis and Mining

SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding

Community Detection in Dynamic Social Networks: A Multi-Agent Aya based on Electric Field

Contact Info

Product

Resources

About