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Post Traumatic Stress Disorder (PTSD) is a public health problem afflicting millions of people each year. It is especially prominent among military veterans. Understanding the language, attitudes, and topics associated with PTSD presents an important and challenging problem. Based on their expertise, mental health professionals have constructed a formal definition of PTSD. However, even the most assiduous...
Millions of users create user profiles on social media. Changes made to an attribute in the user profiles on social media generate a huge volume of data representing a data stream. A framework has been proposed to analyze such data streams and cluster the attribute values related to each other.
Topic Models are statistical models that can be used for discovering the abstract “topics” that may occur in a text corpus, however they face dramatic challenges when coping with very sparse and yet topically diverse micro-blog posts such as tweets. In such streams, not only are the topics very diverse, but also the vocabulary is huge, making the sampling space for generative models vast. In this...
Subgraph isomorphism is a fundamental graph problem with many applications. Due to its NP-Hard nature, subgraph isomorphism in large dynamic graphs is considered as a challenging problem. In this paper, we present a distributed graph pruning algorithm (D-IDS) for dynamic graphs to enable efficient subgraph isomorphism. D-IDS continuously maintains the maximum dual simulation match in a dynamic graph...
Interaction among users on social networks through messages and interested topics forms online communities. The question is how to discover what communities users belong to or what online communities are interested in or what each period of time the interested topic change in online communities are? To answer these questions, this paper proposes a new model for discovering communities on social networks...
The proliferation of location-acquisition devices and thriving development of social websites enable analyzing users' movement behaviors and detecting social events in dynamic trajectory streams. In this paper, we firstly analyze the challenges in trajectory stream clustering, and then depict a three-part framework to deal with this issue, that includes i) trajectory data pre-processing for higher...
Listing relevant patterns from graphs is becoming increasingly challenging as Web and social graphs are growing in size at a great rate. This scenario requires to process information more efficiently, including the need of processing data that cannot fit in main memory. Typical approaches for processing data using limited main memory include the streaming and external memory models. This paper addresses...
Crowd sourcing is emerging as a powerful paradigm to solve a wide range of tedious and complex problems in various enterprise applications. It spawns the issue of finding the unknown collaborative and competitive group of solvers. The formation of collaborative team should provide the best solution and treat that solution as a trade secret avoiding data leak between competitive teams due to reward...
Although a lot of literatures have been proposed on the issue of privacy preserve with relational data, social networks bring new challenges of resisting re-identify attacks. Based on message passing, an approach of privacy preserve in social networks is proposed in this paper. Individuals are assigned to different clusters according to their quasi-identifies and structural similarity measured by...
The(P, α, K) anonymity model for privacy protection of personal information in the social networks is proposed in this paper. The hidden fields P and the hidden levels a are set according to theindividual privacy needs of the users. Then make the released data to meet the privacy protection requirements through the Datafly algorithm and the clustering algorithm. The experimental data shows that the...
Social Networks Service (SNS), is becoming more and more popular and a lot of studies have been carried out in this active field. However, traditional analysis methods based on single machines is not suitable because the network is growing too large. MapReduce, a programming paradigm proposed by Google, gives us a new approach to solve large-scale social networks analysis problem by making use of...
Peer to peer (P2P) systems are extremely vulnerable to Sybil attacks, in which a malicious user controls a large number of Sybil peers to collude to break the system laws. This paper proposes a distributed algorithm, named Sybil Resisting Network Clustering (SRNC), to resist the Sybil attack by preventing honest peers from communicating with Sybil Peers. SRNC is based on a social network model. In...
Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping sub-matrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social networks. Existing co-clustering schemes do not exploit the fact that overlapping factors are often sparse,...
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