Pattern matching with wildcards and gap-length constraints based on a centrality-degree graph
29
Citation
37
Reference
10
Related Paper
Citation Trend
Keywords:
Degree (music)
Degree centrality is considered to be one of the most basic measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasizes the number of connections that an actor has with others. However, it does not accommodate the value of the duration of relations with other actors in a network; and, therefore, this traditional degree centrality approach regards only the presence or absence of links. Here, we introduce a time-variant approach to the degree centrality measure — time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network. We illustrate the difference between traditional and TSDC measure by applying these two approaches to explore the impact of degree attributes of a patient-physician network evolving during patient hospitalization periods on the hospital length of stay (LOS) both at a macro- and a micro-level. At a macro-level, both the traditional and time-scale approaches to degree centrality can explain the relationship between the degree attribute of the patient-physician network and LOS. However, at a micro-level or small cluster level, TSDC provides better explanation while the traditional degree centrality approach is found to be inadequate in explaining its relationship with LOS. Our proposed TSDC measure can explore time-variant relations that evolve among actors in a given social network.
Degree (music)
Katz centrality
Network Analysis
Network theory
Social Network Analysis
Scale-free network
Cite
Citations (17)
We gathered the information about a group of female B. Sc. students at university for preparing their degree centralities. The degree centralities of this group were obtained by social network professional software in seven weeks. Finally the changes of degree centrality in these weeks are presented as charts and their differences were discussed.
Degree (music)
Social Network Analysis
Cite
Citations (2)
In this paper, we introduce a time-variant approach to degree centrality measure - time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network, whereas, the traditional degree centrality approach regards only the presence or absence of links. We illustrate the difference between traditional and time scale degree centrality measure by applying these two approaches to explore the impact of 'degree' attributes of doctor-patient network that evolves during patient hospitalization period on the hospital length of stay (LOS) both in macro- and micro-level. In macro-level, both the traditional and time-scale approaches to degree centrality can explain the relationship between the 'degree' attribute of doctor-patient network and LOS. However, at micro-level or small cluster level, TSDC provides better explanation while traditional degree centrality approach is impotent to explain the relationship between them.
Degree (music)
Katz centrality
Scale-free network
Cite
Citations (13)
Detection of different kinds of anomalous behaviors originating from negative ties among actors in online social networks is an unexplored area requiring extensive research. Due to increase in social crimes such as masquerading, bullying, etc., identification and analysis of these activities has become need of the hour. Approaches from two separate, yet, similar research areas, i.e. anomaly detection and negative tie analysis, can be clubbed together to identify negative anomalous nodes. Use of best measures from centrality based (negative ties) and structure based approaches (anomaly detection) can help us identify and analyze the negative ties more efficiently. A comparative analysis has been performed to detect the negative behaviors in online networks using different centrality measures and their relationship in curve fitting anomaly detection techniques. From results it is observed that curve fitting analysis of centrality measures relationship performs better than independent analysis of centrality measures for detecting negative anomalous nodes.
Identification
Anomaly (physics)
Network Analysis
Social Network Analysis
Cite
Citations (11)
Degree (music)
Cite
Citations (0)
Currently,more and more researches focus on the effects of social relation embedding in economic activities on employee's individual performance. However,no consistent conclusion is drawn. In order to reveal the relationship between network structure and the employee's performance,this paper introduces a new concept——degree heterogeneity. Degree centrality represents the absolute power of individuals in the networks,while degree heterogeneity denotes the distribution of such power. Using the 2009 survey data from Xi'an,we find that the degree centrality has a positive influence on employee's performance in informal networks( not in formal network),while the degree heterogeneity negatively adjusts the relationship between degree centrality and employee's performance.
Degree (music)
Degree distribution
Cite
Citations (0)
Degree (music)
Cite
Citations (18)
Abstract Many of the structural characteristics of a network depend on the connectivity with and within the hubs. These dependencies can be related to the degree of a node and the number of links that a node shares with nodes of higher degree. In here we revise and present new results showing how to construct network ensembles which give a good approximation to the degree–degree correlations, and hence to the projections of this correlation like the assortativity coefficient or the average neighbours degree. We present a new bound for the structural cut–off degree based on the connectivity within the hubs. Also we show that the connections with and within the hubs can be used to define different networks cores. Two of these cores are related to the spectral properties and walks of length one and two which contain at least on hub node, and they are related to the eigenvector centrality. We introduce a new centrality measured based on the connectivity with the hubs. In addition, as the ensembles and cores are related by the connectivity of the hubs, we show several examples how changes in the hubs linkage effects the degree–degree correlations and core properties.
Assortativity
Degree (music)
Degree distribution
Cite
Citations (18)
We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.
PageRank
Degree (music)
Katz centrality
Cite
Citations (4)
In this paper we study how to determine the nodes that most influential to a node in the network. Social Network Analysis (SNA) can measure the centrality of a node in order to obtain an influential nodes in the dissemination of information. One of the centrality measurement that can be applied is degree centrality. In this research, the method used is Opsahl method, combines two indicators, the number of neighborhood (degree) and the amount of weight relations (strength) of a node and uses tuning parameters. The weight relations are obtained from the number of relations as following/follower, mention and reply. Tuning parameters are parameters which are used to set the influence of both degree and strength to the degree centrality measurement results. Based on test results, the node who has a high strength value is derived from weight relations which are obtained from mentions and replies.
Degree (music)
Katz centrality
Value (mathematics)
Social Network Analysis
Cite
Citations (46)