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In self-regulated learning, evaluation is a complex task of the teaching process, but even more if students have social media that allow them to build their personal learning environment in different ways. In these kind of virtual environments a large amount of data that needs to be assessed by teachers is generated, and therefore they require tools that facilitate the assessment task. In this paper,...
Hierarchical agglomerative algorithm is widespread used in community detection of social networks. This paper explores an enhanced similarity which is based on interactive behavior of social members. The enhanced similarity expands the concept of similarity from vertexes to communities in the social network. Furthermore, the hierarchical agglomerative algorithm has been applied and the enhanced similarity...
Community finding in social network analysis is the task of identifying groups of people within a larger population who are more likely to connect to each other than connect to others in the population. Much existing research has focussed on non-overlapping clustering. However, communities in real-world social networks do overlap. This paper introduces a new community finding method based on overlapping...
Large-scale social networks emerged rapidly in recent years. Social networks have become complex networks. The structure of social networks is an important research area and has attracted much scientific interest. Community is an important structure in social networks. In this paper, we propose a community detection algorithm based on seed nodes. First, we introduce how to find seed nodes based on...
As the research of the complex network is becoming more and more hot in recent years, a lot of different characteristics have been found in the research of complex network, such as small-world property and scale-free properties. Community structure is one of the most relevant features of complex networks as well. Community, in which vertices are joined tightly together, between which there are only...
In this paper, for overlapping community detection, we propose a novel framework of the link-space transformation that transforms a given original graph into a link-space graph. Its unique idea is to consider topological structure and link similarity separately using two distinct types of graphs: the line graph and the original graph. For topological structure, each link of the original graph is mapped...
Clustering in networks/graphs is an important problem with applications in the analysis of gene-gene interactions, social networks, text mining, to name a few. Spectral clustering is one of the more popular techniques for such purposes, chiefly due to its computational advantage and generality of application. The algorithm's generality arises from the fact that it is not tied to any modeling assumptions...
Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. Community mining or detection involves discovering groups in a network where an individual's group memberships are not explicitly given. Much of the research on detecting communities in co-authorship networks has been done using publicly available datasets....
The huge amount of check-in data obtained through location-based social networks (LBSNs) provides a great opportunity to learn the characteristics of a geographic area through the users' collective check-in behavior. In this paper, we explore structure analysis of place networks, in which vertices are geographic places while the links between places are formed based on the user's check-in history...
The hidden knowledge in the information network has attracted a large number of researchers from different subjects such as sociology, physics and computer science. Community discovery has great significance for the analysis of information network structure, the understanding of its function, the discovery of its hidden patterns, and the predication of its behavior. In the practical life, people tend...
As community detection having been a hot issue in recent years, firstly the correlate clustering technologies based on local information are studied and summarized. Secondly, the label propagation algorithm that is short for LPA is researched and analyzed in depth. Finally, for the random strategy of LPA results that the network partition is always not optimal, we introduces the concept of the similarity...
The development of several popular social networks in recent days and publication of social network data has led to the danger of disclosure of sensitive information of individuals. This necessitated the preservation of privacy before the publication of such data. There are several algorithms developed to preserve privacy in micro data. But these algorithms cannot be applied directly as in social...
One of the most obvious features of social networks is their community structure. Several types of methods were developed for discovering communities in the networks, either from the global perspective or based on local information only. Local methods are appropriate when working with large and dynamic networks or when real-time results are expected. In this paper we explore two such methods and compare...
Spectral clustering is a modern data clustering methodology with many notable advantages. However, this method has a weakness in that it requires researchers to specify a priori the number of clusters. In most cases, it is a challenge to know the number of clusters accurately. Here, we propose a novel way to solve this problem by involving the concept of group leaders and members from social network...
The hidden knowledge in the information network has attracted a large number of researchers from different subjects such as sociology, physics and computer science. Community discovery has great significance for the analysis of information network structure, the understanding of its function, the discovery of its hidden patterns, and the predication of its behavior. In the practical life, people tend...
While the detection of social subgroups (i.e., communities) has always been a fundamental task in social network analysis, few efforts has been made to characterize the detected community. Meanwhile, to effectively facilitate applications based on the community structure, it is very important to understand the features of each community. Thereby, a systematic community profiling mechanism is needed...
This paper introduces an agglomerative method for detecting cohesive subgroups in networks based on geodesic distance. The algorithm starts with a set of nodes as "seed". Beginning with the seed nodes as initial clusters, the clusters grow by incorporating more nodes successively based on minimal average distance to the current members of the cluster as a criterion for cluster extension...
In this paper, we investigate the problem of automatically constructing characters¡¦ social network from movies. Unlike existing approaches that use co-appearance information to measure the relationship between two characters, we argue that a method that describes the characters¡¦ interaction, rather than the co-appearance, makes more sense. We propose a new scheme that quantifies the interaction...
This project is intended to put forward a new model and algorithm to deal with graph partitioning, which is an attractive part in the field of social network analysis. In the recent years, an exponent increasing number of studies have been undertaken to process social network data, partly as a result of the fact that so much social network data has become available. Another reason is that the significance...
In the paper we present a cluster based approach for terrorist network evolution. We have applied hierarchical agglomerative clustering approach to 9/11 case study. We show that, how individual actors who are initially isolated from each other are converted in small clusters and result in a fully evolved network. This method of network evolution can help intelligence security analysts to understand...
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