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Dynamic community detection has been of great significance on analyzing network structure and community evolution. Among state-of-the-art methods, incremental algorithms based on modularity have been used widely, for the fully utilization of both current and historical information. Unfortunately, they are difficult to uncover small community due to problem called “resolution limit” and also sensitive...
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information...
In this work we present a new local, vertex-level measure of community change. Our measure detects vertices that change community membership due to the actions (edges) of a vertex itself and not only due to global community shifts. The local nature of our measure is important for analyzing real graphs because communities may change to a large degree from one snapshot in time to the next. Using both...
Online dating arises as a popular venue for finding romantic partners in recent years. Many online dating sites adopt recommender systems to help their users. However, few of current research provides solutions to cold start problem, i.e., providing recommendations to new users. In this research, we propose a new approach of providing reciprocal online dating recommendations to new users. Specifically,...
Friendster is a social networking service which used to be popular all over the world at the beginning of the 21st century and declined thereafter. Some researchers have examined the process of decline and explained that the social network on Friendster collapsed from the outside of the core structure. In order to verify if their assertion is true, we analyze the time evolution of the network structure...
While online social networks provide access to a massive information source, they also enable wide dissemination of false or inaccurate content. Undesirable results caused by misinformation propagation make its timely detection very imperative. An important question is how many monitors are required to detect all misinformation cascades at their early stage. To answer this question, we define a Time...
Dynamic graphs are used to represent changing relational data. In order to create a dynamic graph representing relationships or interactions over time, it is necessary to choose a method of adding new data and removing, or otherwise de-emphasizing, past data to decrease its influence. In particular, the question of aging edges is new to dynamic graphs and has not been thoroughly studied. In this work,...
Blockmodelling is a technique whose aim is to identify meaningful structure in networks. Community finding is a type of blockmodelling in so far as it focuses on identifying dense subgraph structure. Generalised blockmodelling allows an analyst to explicitly control the type of extracted structure. When compared to the well studied community-finding problem, generalised blockmodelling algorithms lag...
User location is crucial in understanding the dynamics of user activities, especially in relating their online and offline aspects. However, users' social media activities, such as tweets sent, do not always reveal their location. We consider the problem of estimating geo-tags for tweets and develop a comprehensive approach that incorporates textual content, the user's personalized behavior, and the...
Determining the frequencies and the distribution of small subgraph patterns in a large input graph is an important part of many graph based mining tasks such as Frequent Subgraph Mining (FSM) and Motif Detection. Due to the exponential number of such graph patterns the interpretation of the mining results is mostly limited to finding unexpectedly frequent patterns, and in general identifying few particularly...
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