Meerkat: Community Mining with Dynamic Social Networks

Chen, Jian-Zhang; Fagnan, Justin; Goebel, Randy; Rabbany, Reihaneh; Sangi, Farzad; Takaffoli, Mansoureh; Verbeek, Eric; Zaı̈ane, Osmar R.

doi:10.1109/icdmw.2010.40

Cited by 17 publications

(5 citation statements)

References 11 publications

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“…Some SNA tools implement similar functionalities to these described in this paper, to a greater or lesser extent. Meerkat is a tool for visualization and community mining of social networks [ 31 ]. It uses an approach based on the concepts of filtering and extraction in order to obtain sub-graphs, which can be later used to study some specific social sub-networks where only some individuals are involved.…”

Section: Discussionmentioning

confidence: 99%

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies

Benítez-Andrades

Rodríguez-González

Benavides

et al. 2018

IJERPH

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Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network-generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error-prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called “ontology” that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

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Section: Discussionmentioning

confidence: 99%

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies

Benítez-Andrades

Rodríguez-González

Benavides

et al. 2018

IJERPH

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“…Many previous works extensively study symmetric similarity measurements such as Dice's coefficient, Jaccard's coefficient, cosine similarity, etc. [12]. However, these studies are hardly applicable to our situation because we think asymmetric similarity is characteristic of a social opinion-based system.…”

Section: Two Asymmetric Measurementsmentioning

confidence: 97%

Crowdsourcing recommendations from social sentiment

Xie

Honbo

Zhang

et al. 2012

Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining

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In this paper, we investigate an innovative recommendation system by incorporating relevant social opinion and sentiment information. Our recommendation system, a powerful application of social sentiment analysis, differs from many existing models, which investigate the situation where the social network itself is structured to work with the product ranking and is specially built inside an e-commerce website. In contrast, our proposed system focuses on constructing and inferring product recommendations from external social network services (SNS) such as Facebook. In our system, we process product features in a finite-dimensional polynomial linear space. Additional components of our proposed system include an asymmetric similarity measurement and an asymmetric advantage measurement. We also show that our definitions for the two measurements include specific properties that reduce the computational overhead in the experiments. An important aspect of our modeling is to incorporate user-generated high-level semantic sentiment in the analysis. We apply our models to real time data and observe promising results for not only product recommendation but also job recommendation.

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“…Hence, one problem that needs to be dealt with when visualizing such an object is simplification. Many visualization approaches and in particular interactive visualization tools solve this problem by offering different views on different aspects of the object [32,53,125]. Apart from different views, the most intuitive layout for evolving clusterings is probably to draw consecutive snapshots next to each other and depict the correspondence of clusters for example by colors or by additional edges between the snapshots.…”

Section: Main Issues When Clustering Evolving Networkmentioning

confidence: 99%

Clustering Evolving Networks

Hartmann

Kappes

Wagner

2016

Lecture Notes in Computer Science

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Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online scenarios, i.e., approaches that incrementally use structural information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are often used for evaluation.

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Meerkat: Community Mining with Dynamic Social Networks

Cited by 17 publications

References 11 publications

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies

Crowdsourcing recommendations from social sentiment

Clustering Evolving Networks

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