HOCTracker: Tracking the Evolution of Hierarchical and Overlapping Communities in Dynamic Social Networks

Bhat, Sajid Yousuf; Abulaish, Muhammad

doi:10.1109/tkde.2014.2349918

Cited by 58 publications

(25 citation statements)

References 22 publications

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“…(14). W which captures the correlation level between the communities and the generative features, is updated by FindDerivationW and UpdateW according to equations (15) and (16). β is updated based on equations (17) and (18).…”

Section: Pfcd Algorithmmentioning

confidence: 99%

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare

Hajiabadi

Jalili

2021

IEEE Trans. Knowl. Data Eng.

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Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

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Section: Pfcd Algorithmmentioning

confidence: 99%

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare

Hajiabadi

Jalili

2021

IEEE Trans. Knowl. Data Eng.

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“…Though topological features for each node (user) in the users' interactions network are defined using graph properties, an overlapping community structure of nodes is identified to define community-based features [4]. The community-based features include the features that express the role of a node in the community structure, i.e., whether a node is a boundary node or a core node, and the number of communities a particular node belongs to.…”

Section: Topological and Community-based Featuresmentioning

confidence: 99%

“…Finally, both topological and community-based features are extracted from the resultant network with injected spammers to learn various classifier ensembles. In order to extract community-based features, one of our density-based overlapping community detection algorithms proposed in [4] is applied to identify overlapping community structures in the social network.…”

Section: Data Setmentioning

confidence: 99%

Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Abulaish

Bhat

2015

Foundations of Computing and Decision Sciences

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Abstract.As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users' interaction network and uses popular classifier ensembles -bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.

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“…Recent methods [3], [4], [5], model the graph as a series of frozen networks, where each network corresponds to a particular point in time. Such modeling has been useful to detect structural changes in the network [4], [5], [6], [7], [8] and to reveal important network information. To detect changes in dynamic social networks, two major approaches have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors use a global approach [3], [9], [10], in which the complete network is tracked over time to observe how nodes and edges behave. Others [4], [5], [7], [8] focus their efforts on tracking communities over time. In this paper, our focus is on the evolution of communities over time.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

He¹,

Tajeuna

Wang

et al. 2017

2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.

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HOCTracker: Tracking the Evolution of Hierarchical and Overlapping Communities in Dynamic Social Networks

Cited by 58 publications

References 22 publications

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

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