25 research outputs found
A Faster Method to Estimate Closeness Centrality Ranking
Closeness centrality is one way of measuring how central a node is in the
given network. The closeness centrality measure assigns a centrality value to
each node based on its accessibility to the whole network. In real life
applications, we are mainly interested in ranking nodes based on their
centrality values. The classical method to compute the rank of a node first
computes the closeness centrality of all nodes and then compares them to get
its rank. Its time complexity is , where represents total
number of nodes, and represents total number of edges in the network. In
the present work, we propose a heuristic method to fast estimate the closeness
rank of a node in time complexity, where . We
also propose an extended improved method using uniform sampling technique. This
method better estimates the rank and it has the time complexity , where . This is an excellent improvement over the
classical centrality ranking method. The efficiency of the proposed methods is
verified on real world scale-free social networks using absolute and weighted
error functions
Degree Ranking Using Local Information
Most real world dynamic networks are evolved very fast with time. It is not
feasible to collect the entire network at any given time to study its
characteristics. This creates the need to propose local algorithms to study
various properties of the network. In the present work, we estimate degree rank
of a node without having the entire network. The proposed methods are based on
the power law degree distribution characteristic or sampling techniques. The
proposed methods are simulated on synthetic networks, as well as on real world
social networks. The efficiency of the proposed methods is evaluated using
absolute and weighted error functions. Results show that the degree rank of a
node can be estimated with high accuracy using only samples of the
network size. The accuracy of the estimation decreases from high ranked to low
ranked nodes. We further extend the proposed methods for random networks and
validate their efficiency on synthetic random networks, that are generated
using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be
efficiently used for random networks as well
A Survey on Studying the Social Networks of Students
Do studies show that physical and online students' social networks support
education? Analyzing interactions between students in schools and universities
can provide a wealth of information. Studies on students' social networks can
help us understand their behavioral dynamics, the correlation between their
friendships and academic performance, community and group formation,
information diffusion, and so on. Educational goals and holistic development of
students with various academic abilities and backgrounds can be achieved by
incorporating the findings attained by the studies in terms of knowledge
propagation in classroom and spread of delinquent behaviors. Moreover, we use
Social Network Analysis (SNA) to identify isolated students, ascertain the
group study culture, analyze the spreading of various habits like smoking,
drinking, and so on. In this paper, we present a review of the research showing
how analysis of students' social networks can help us identify how improved
educational methods can be used to make learning more inclusive at both school
and university levels and achieve holistic development of students through
expansion of their social networks, as well as control the spread of delinquent
behaviors.Comment: Huso 201