Identification of influential nodes in social networks with community structure based on label propagation
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Identification
Maximization
Social network (sociolinguistics)
Evolving networks
Dynamic network analysis
Network Structure
Science is a complex system. Building on Latour's actor network theory, we model published science as a dynamic hypergraph and explore how this fabric provides a substrate for future scientific discovery. Using millions of abstracts from MEDLINE, we show that the network distance between biomedical things (i.e., people, methods, diseases, chemicals) is surprisingly small. We then show how science moves from questions answered in one year to problems investigated in the next through a weighted random walk model. Our analysis reveals intriguing modal dispositions in the way biomedical science evolves: methods play a bridging role and things of one type connect through things of another. This has the methodological implication that adding more node types to network models of science and other creative domains will likely lead to a superlinear increase in prediction and understanding.
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Dynamic network analysis
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Network embeddings learn to represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of the network for network analysis. The temporal edges (e.g., relationships, contacts, and emails) in dynamic networks are important for network evolution analysis, but few existing methods in network embeddings can capture the dynamic information from temporal edges. In this paper, we propose a novel dynamic network embedding method to analyze evolution patterns of dynamic networks effectively. Our method uses random walk to keep the proximity between nodes and applies dynamic Bernoulli embeddings to train discrete-time network embeddings in the same vector space without alignments to preserve the temporal continuity of stable nodes. We compare our method with several state-of-the-art methods by link prediction and evolving node detection, and the experiments demonstrate that our method generally has better performance in these tasks. Our method is further verified by two real-world dynamic networks via detecting evolving nodes and visualizing their temporal trajectories in the embedded space.
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Network Analysis
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There exists a wide variety of complex networks, ranging from biology to sociology. Various real world networks can be depicted as complex networks. These networks have different structures and communication patterns. In this paper we have discussed their structures and corresponding examples that demonstrates the behavior of the networks. The structure is helpful to recognize the communication in a network. Earlier, there were small networks with few vertices and edges. But nowadays large complex networks have come into existence. Many researchers are trying to unfold the characteristics which will help to understand the complex networks in a better way. In this paper we have discussed various properties and their effects on different networks. These properties define the non-random nature of complex networks. They have significant impact on the network's structure. We have tried to describe complex network structures from various researchers' point of view.
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Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. Social network theory and methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to studying such dynamic network formation processes to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge regards link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this
Dynamic network analysis
Organizational network analysis
Network Formation
Social Network Analysis
Social network (sociolinguistics)
Evolving networks
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Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.
Snapshot (computer storage)
Dynamic network analysis
Stochastic block model
Evolving networks
Jaccard index
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At present, the study of complex networks include the geometric nature of the network, the formation mechanism of the network, the statistical law of the network evolution, the model property on the network, the structure stability of the network, and other issues like network evolution and dynamic mechanics, etc. There into, in the field of natural science, the basic measuring of the network research includes degree and its distribution characteristic, relevancy of degree, clustering and its distribution characteristics, shortest path and its distribution characteristics, sparsity and its distribution characteristics, and size distribution of connected groups. In order to depict the complex network topology, scholars have proposed many methods to describe the statistical parameter and measurement for complex network features. The OSN will be briefly analyzed below by using these important concepts. At last, the research significance of the online social network, the value of theory, potential applications and research direction in future have been summed up.
Degree distribution
Network Formation
Clustering coefficient
Average path length
Evolving networks
Dynamic network analysis
Organizational network analysis
Scale-free network
Social network (sociolinguistics)
Preferential attachment
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With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. Being it's objective the investigation of social structures through the use of networks and graph theory, Social Network Analysis is, nowadays, an important research area in several domains. Social Network Analysis cope with different problems namely network metrics, models, visualization and information spreading, each one with several approaches, methods and algorithms. One of the critical areas of Social Network Analysis involves the calculation of different centrality measures (i.e.: the most important vertices within a graph). Today, the challenge is how to do this fast and efficiently, as many increasingly larger datasets are available. Recently, the need to apply such centrality algorithms to non static networks (i.e.: networks that evolve over time) is also a new challenge. Incremental and dynamic versions of centrality measures are starting to emerge (betweenness, closeness, etc). Our contribution is the proposal of two incremental versions of the Laplacian Centrality measure, that can be applied not only to large graphs but also to, weighted or unweighted, dynamically changing networks. The experimental evaluation was performed with several tests in different types of evolving networks, incremental or fully dynamic. Results have shown that our incremental versions of the algorithm can calculate node centralities in large networks, faster and efficiently than the corresponding batch version in both incremental and full dynamic network setups.
Social Network Analysis
Dynamic network analysis
Network theory
Closeness
Network Analysis
Evolving networks
Social network (sociolinguistics)
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With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. Being it's objective the investigation of social structures through the use of networks and graph theory, Social Network Analysis is, nowadays, an important research area in several domains. Social Network Analysis cope with different problems namely network metrics, models, visualization and information spreading, each one with several approaches, methods and algorithms. One of the critical areas of Social Network Analysis involves the calculation of different centrality measures (i.e.: the most important vertices within a graph). Today, the challenge is how to do this fast and efficiently, as many increasingly larger datasets are available. Recently, the need to apply such centrality algorithms to non static networks (i.e.: networks that evolve over time) is also a new challenge. Incremental and dynamic versions of centrality measures are starting to emerge (betweenness, closeness, etc). Our contribution is the proposal of two incremental versions of the Laplacian Centrality measure, that can be applied not only to large graphs but also to, weighted or unweighted, dynamically changing networks. The experimental evaluation was performed with several tests in different types of evolving networks, incremental or fully dynamic. Results have shown that our incremental versions of the algorithm can calculate node centralities in large networks, faster and efficiently than the corresponding batch version in both incremental and full dynamic network setups.
Social Network Analysis
Dynamic network analysis
Closeness
Network Analysis
Network theory
Evolving networks
Social network (sociolinguistics)
Organizational network analysis
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Information networks that describe the relationship between individuals are called social networks and are usually modeled by a graph structure. Social network analysis is the study of these information networks which leads to uncover patterns of interaction among the entities. Most social networks are dynamic, and studying the evolution of these networks over time could provide insight into the changes that occurred in the iteration patterns and also the future trends of the networks. Furthermore, in a dynamic scenario, communities, which are groups of densely interconnected nodes, are affected by changes in the underlying population. The analysis of communities and their evolutions can help determine the characteristics and structural properties of the network. Here, we provide a brief overview of the existing research in the area of dynamic social network analysis, their limitations, and the challenges that are exists for further analysis.
Dynamic network analysis
Social Network Analysis
Evolving networks
Social network (sociolinguistics)
Network Analysis
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Research on complex networks has tightly relation with social network analysis.In fact,the fast growth of network science in the past decade is fertilized by the basic concepts and methods established by social network analysis.Meanwhile,social network analysis is also helped by the network science.This paper has briefly reviewed some of recent progress in the related topics done by our groups.The topics include cooperation-competition networks,the definition,detecting methods and index of significance of community structures,spatial structure of social networks and their consequence on the structure and dynamics of networks.
Social Network Analysis
Organizational network analysis
Social network (sociolinguistics)
Network Analysis
Dynamic network analysis
Network theory
Network Structure
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