Sajid Yousuf Bhat

Community-based features for identifying spammers in online social networks

The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.

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

In this paper, we propose a unified framework, HOCTracker, for tracking the evolution of hierarchical and overlapping communities in online social networks. Unlike most of the dynamic community detection methods, HOCTracker adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures, and automatically tracks evolutionary events like birth, growth, contraction, merge, split, and death of communities. It uses a novel and efficient log-based approach to map evolutionary relations between communities identified at two consecutive time-steps of a dynamic network, which considerably reduces the number of community comparisons. Moreover, it does not require an ageing function to remove old interactions for identifying community evolutionary events. HOCTracker is applicable to directed/undirected and weighted/unweighted networks. Experimental results have shown that community structures identified by HOCTracker on some well-known benchmark networks are significant and in general better that the community structures identified by the state-of-the-art methods.

Using communities against deception in online social networks

Online social networking (OSN) sites such as Facebook and Twitter have become highly popular on the Internet with millions of members sharing information and content, and connecting with each other. The connections thus established reflect the real-world relationships between the users of these social networks. These sites are being looked upon as high-potential marketing opportunities by many organisations. OSNs offer many useful properties that reflect real-world social network characteristics, which include small-world behaviour, significant local clustering, the existence of large, strongly connected components and formation of tightly knit groups or communities. 1 , 2 , 3 Malicious activities in online social networks (OSNs) have transformed from simple spamming to highly deceptive forms focused on breaching the privacy of online social network users and ultimately their trust. Traditional content-based and collaborative filtering techniques give only average results. The topological characteristics of legitimate users, including the formation of tightly knit communities, is a more promising approach, but we need to devise efficient techniques for identifying spammers and attackers, explain Sajid Yousuf Bhat and Muhammad Abulaish of Jamia Millia Islamia, New Delhi.

Analysis and mining of online social networks: emerging trends and challenges

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semantic SNA, information diffusion, viral marketing, and influential node mining. WIREs Data Mining Knowl Discov 2013, 3:408–444. doi: 10.1002/widm.1105

OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs

In this paper, we propose a unified framework OCTracker for tracking overlapping community evolution in online social networks. OCTracker adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures and automatically detects evolutionary events like birth, growth, contraction, merge, split, and death of communities with time. Unlike other density-based community detection methods, the proposed method does not require the neighborhood threshold parameter to be set by the users, rather it automatically determines the same for each node locally.

Overlapping Social Network Communities and Viral Marketing

Social networks have highly been used to understand the behavior and activities of individuals in nature and society. They are being used as a means to communicate, diffuse information, and to control the spread of diseases and computer viruses, in addition to many other tasks. Business organizations look upon social networks as an opportunity to spread the word-of-mouth for viral marketing and this task has gained significance with the popularity of Online Social Networks (OSNs). However, an important characteristic of social networks, including OSNs, which is the existence of overlapping communities of users, has not been exploited yet for the task of viral marketing even though it seems promising. This paper aims to present the importance of identifying overlapping communities for the task of viral marketing in social networks and also provides some experimental results on an email network to back the claims.

OCMiner: A density-based overlapping community detection method for social networks

Community detection is an important task for identifying the structure and function of complex networks. The task is challenging as communities often show overlapping and hierarchical behavior, i.e., a node can belong to multiple communities, and multiple smaller communities can be embedded within a larger community. Moreover, real-world networks often contain communities of arbitrary size and shape, along with outliers. This paper presents a novel density-based overlapping community detection method, OCMiner, to identify overlapping community structures in social networks. Unlike other density-based community detection methods, OCMiner does not require the neighborhood threshold parameter (e) to be set by the users. Determining an optimal value for e is a longstanding and challenging task for density-based clustering methods. Instead, OCMiner automatically determines the neighborhood threshold parameter for each node locally from the underlying network. It also uses a novel distance function which utilizes the weights of the edges in weighted networks, besides being able to find communities in un-weighted networks. The efficacy of the proposed method has been established through experiments on various real-world and synthetic networks. In comparison to the existing state-of-the-art community detection methods, OCMiner is computationally faster, scalable to large-scale networks, and able to find significant community structures in social networks.

A density-based approach for mining overlapping communities from social network interactions

In this paper, we propose a density-based community detection method, CMiner, which exploits the interaction graph of online social networks to identify overlapping community structures. Based on the average reciprocated interactions of a node in the network, a new distance function is defined to find the similarity between a pair of nodes. The proposed method also provides a basic solution for automatic determination of the neighborhood threshold, which is a non-trivial problem for applying density-based clustering methods. Considering the local neighborhood of a node p, the distance function is used to determine the distance between the node p and its neighbors in the interaction graph to identify core nodes, which are then used to define overlapping communities. On comparing the experimental results with clique percolation and other related methods, we found that CMiner is comparable to the state-of-the-art methods and is also computationally faster.

Classifier ensembles using structural features for spammer detection in online social networks

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.

A Density-Based Approach to Detect Community Evolutionary Events in Online Social Networks

With the advent of Web 2.0/3.0 supported social media, Online Social Networks (OSNs) have emerged as one of the popular communication tools to interact with similar interest groups around the globe. Due to increasing popularity of OSNs and exponential growth in the number of their users, a significant amount of research efforts has been diverted towards analyzing user-generated data available on these networks, and as a result various community mining techniques have been proposed by different research groups. But, most of the existing techniques consider the number of OSN users as a fixed set, which is not always true in a real scenario, rather the OSNs are dynamic in the sense that many users join/leave the network on a regular basis. Considering such dynamism, this chapter presents a density-based community mining method, OCTracker, for tracking overlapping community evolution in online social networks. The proposed approach adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures and automatically detects evolutionary events including birth, growth, contraction, merge, split, and death of communities with time. Unlike other density-based community detection methods, the proposed method does not require the neighborhood threshold parameter to be set by the users, rather it automatically determines the same for each node locally. Evaluation results on various datasets reveal that the proposed method is computationally efficient and naturally scales to large social networks.