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Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have been extended to networks with multiple attributes for each node in the real-world. In a multi-attributed network, often, a cluster of nodes is only interesting for a subset (subspace) of attributes, andthis type of clusters...
Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar...
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...
Social network analysis is an important set of techniques that are used in many different areas. One such area is intelligence and law enforcement where social network analysis is used to study various kinds of networks. One of the problems with social networks that are extracted from social media is that easily becomes very large and as a consequence difficult to analyze. Therefore, there is a need...
Social networking portals serve as an ideal platform for a person or an organization, to accomplish self-presentation and self-enhancement goals there by to understand their social relevance and hence, there have been many studies attempting to identify the relationship between different aspects of social media articles. Machine learning methods play a critical role in social media data analytics...
Due to the emerging Big Data paradigm, traditional data management techniques result inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to social interactions among users calls for advanced analysis strategies. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch...
Topological data analysis is a noble method to analyze high-dimensional qualitative data using a set of properties from topology. In this paper, we explore the feasibility of topological data analysis for mining social media data by investigating the problem of image popularity. We randomly crawl images from Instagram, convert their captions to 300 dimensional numerical vectors using Word2vec, calculate...
Friend recommendation service is a common and important demand for the users on various online platforms. Current studies mainly focus on making predictions with the neighborhood and path information derived from the personal relationship networks. However, the formed links do not indicate that two users are familiar with each other nor have intimate connections. Selective treatments are made according...
With the development of internet, users can express their attitudes towards public events on the social media. Monitoring and analyzing the public opinion can provide effective support for government's policy making. In this paper, a novel online public opinion (OPO) analysis platform over multi-source text streams is proposed. This OPO platform contains three layers: data collection layer, data process...
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...
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.
We present a new multi-level graph drawing algorithm based on the k-core coarsening, a well-known cohesive subgroup analysis method in social network analysis. The k-core of a graph is also known as the degeneracy in graph theory, and can be computed in linear time. Our k-core based multi-level algorithm also includes a new concentric circle placement and a variation of force-directed layout to display...
The problem of node classification has been widely studied in a variety of network-based scenarios. In this paper, we will study the more challenging scenario in which some of the edges in a content-based network are labeled, and it is desirable to use this information in order to determine the labels of other arbitrary edges. Furthermore, each edge is associated with text content, which may correspond...
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...
One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection algorithms. However, it usually leads to high computation complexity. In this paper, a novel algorithm based on the MapReduce model and the label propagation progress with the strategy of incremental related vertices is proposed, which is called PLPIRV (Parallel...
The structure of community represents the latent social context of user activities and has important implementations in the field of collaborative filtering in recommendation systems, in particular; when the recommended items can be inferred based on all users' interests within a community. The important issue when there are some social networks which have no ability to division due to high connectivity...
Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion...
The evolution of Social networking sites has posed lot of challenges for technology firms and researchers [1]. The Social networking sites are gaining popularity amongst users across the globe and networking of individuals is increasing very rapidly. People search on the Social networking sites to find old friends and other interesting people, this search operation runs in the background of the Social...
Swarm intelligence is defined as the properties of artificial systems. It is suited to depict people's daily behavior, such as social media users' behavior. Social media users have some characteristics. For one thing, users who have the same interest will focus on the same VIP (Very Important Person) users inside the industry. For another, the users concentrating on the same VIP user may focus on...
Finding the key nodes of the network is one of the most important applications in Community Detection. This paper combined Centrality, Cluster Coefficient and PageRank ideas, establish the measurement system of key nodes and according to the Kendal correlation coefficient of each index to modify the measurement system. Finally, using the Fagin algorithm to carry on the key nodes of the multidimensional...
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