Advances in Social Networks Analysis and Mining 

About: Advances in Social Networks Analysis and Mining is an academic conference. The conference publishes majorly in the area(s): Social network & Social media. Over the lifetime, 2178 publications have been published by the conference receiving 30689 citations.

Tracking the Evolution of Communities in Dynamic Social Networks

Abstract: Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.

Co-author Relationship Prediction in Heterogeneous Bibliographic Networks

Abstract: The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, \i.e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (\e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called\emph{Path Predict}, \i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.

Prying Data out of a Social Network

Abstract: Preventing adversaries from compiling significant amounts of user data is a major challenge for social network operators. We examine the difficulty of collecting profile and graph information from the popular social networking website Facebook and report two major findings. First, we describe several novel ways in which data can be extracted by third parties. Second, we demonstrate the efficiency of these methods on crawled data. Our findings highlight how the current protection of personal data is inconsistent with user's expectations of privacy.

Studying User Footprints in Different Online Social Networks

Abstract: With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You Tube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.

Stop clickbait: detecting and preventing clickbaits in online news media

Abstract: Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link Such headlines are known as Clickbaits While these baits may trick the readers into clicking, in the long-run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines The extension also offers each reader an option to block clickbaits she doesn't want to see Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits