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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...
Online social networks offer a rich data source for analyzing diffusion processes including rumor and viral spreading in communities. While many models exist, a unified model which enables analytical computation of complex, nonlinear phenomena while considering multiple factors was only recently proposed. We design an optimized implementation of the unified model of influence for vertex centric graph...
Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
Link clustering groups different edges in a graph according to their similarities. Link clustering can reveal the overlapping and hierarchical organizations in a wide spectrum of networks. This work studies how to improve efficiency of link clustering along three dimensions, algorithm, modeling, and parallelization, on multi-core machines. We evaluate the efficiency improved due to each of the three...
Community detection is an important field in research of social networks. There exist a lot of algorithms which most of them are based on the density of connections between groups of nodes. On the one hand, the error and lack of links may lead to great impact on the result of community detection. On the other hand, there are users with deep relation but without much communication, so the density of...
Social network can be viewed as a relationship between the set of connected entities, represented by a large graph consisting of vertices and edges. Dynamicity of the social network demands the isolation and/or incorporation of associated entities and represented by large graph. Depending on the application, some nodes of the graph play the influential roles accordingly and are classified as influential...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
In this paper, an Aggregation-Division Model (ADM) is proposed to simulate the clustering characteristics of nodes in cyberspace and to describe the dynamic evolution process of the community system structure. Based on Cluster-Cluster Aggregation (CCA) model, using the random walk and collision of discrete single particle to simulate the communication and relationship establishment of nodes in social...
The flood of real time social data, generated by various social media applications and sensors, is enabling researchers to gain critical insights into important social modeling and analysis problems such as the evolution of social relationships and analysis of emergent social processes. However, current computational tools have to address the grand challenge of analyzing large and dynamic social networks...
In this study, we investigate the problem of network completion by considering the similarities between the node attributes. Given a sample of observed nodes with their incident edges, how can we efficiently reconstruct the network by completing the missing edges of unobserved nodes? Apart from the missing edges, in real settings the node attributes may be partially missing, as well as they may introduce...
The large number of SNS users brings marketers and managers huge opportunities and tough challenges simultaneously to extract managerial implications from SNS user behaviors. To gain insight into user behaviors, researchers divide users into roles (i.e. user groups) to analyze the difference of user behaviors between distinct roles. In traditional role discovery algorithms, the number of roles is...
Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of...
In this paper a proposed work is based on topic sensitivity which is used to form a community by which effective influence node can be elected in online social network for information diffusion. It is effective route for performing campaign in online social network environment. In which it has to be started from the influence node selection, after that effective information diffusion pattern is identified...
Prime intends of web mining is to mine valuable information and knowledge from web. Social network analysis has become a very well-liked field of research as it is functional for many applications. In this study we will examine the existing soft computing techniques in the area of web mining. We develop efficient methods and algorithms using soft computing approaches. Our Framework will base on Hybrid...
In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users' communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as question-and-answer (Q&A) sites or forums, have no explicit...
Given a set of n entities to be classified, and a matric of dissimilarities between pairs of them. This paper considers the problem called Minimum Sum of Diameters Clustering Problem, where a partition of the set of entities into k clusters such that the sum of the diameters of these clusters is minimized. Brucker showed that the complexity of the problem is NP-hard, when k ≥ 3 [1]. For the case of...
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...
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study,...
Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping...
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...
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