5,194 research outputs found
Closeness centrality in some splitting networks
A central issue in the analysis of complex networks is the assessment of their robustness and vulnerability. A variety of measures have been proposed in the literature to quantify the robustness of networks, and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. \textit{Centrality} parameters play an important role in the field of network analysis. Numerous studies have proposed and analyzed several \textit{centrality} measures. We consider \textit{closeness centrality} which is defined as the total graph-theoretic distance to all other vertices in the graph. In this paper, closeness centrality of some splitting graphs is calculated, and exact values are obtained
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support
Internet and online-based social systems are rising as the dominant mode of
communication in society. However, the public or semi-private environment under
which most online communications operate under do not make them suitable
channels for speaking with others about personal or emotional problems. This
has led to the emergence of online platforms for emotional support offering
free, anonymous, and confidential conversations with live listeners. Yet very
little is known about the way these platforms are utilized, and if their
features and design foster strong user engagement. This paper explores the
utilization and the interaction features of hundreds of thousands of users on 7
Cups of Tea, a leading online platform offering online emotional support. It
dissects the level of activity of hundreds of thousands of users, the patterns
by which they engage in conversation with each other, and uses machine learning
methods to find factors promoting engagement. The study may be the first to
measure activities and interactions in a large-scale online social system that
fosters peer-to-peer emotional support
Network Analysis with the Enron Email Corpus
We use the Enron email corpus to study relationships in a network by applying
six different measures of centrality. Our results came out of an in-semester
undergraduate research seminar. The Enron corpus is well suited to statistical
analyses at all levels of undergraduate education. Through this note's focus on
centrality, students can explore the dependence of statistical models on
initial assumptions and the interplay between centrality measures and
hierarchical ranking, and they can use completed studies as springboards for
future research. The Enron corpus also presents opportunities for research into
many other areas of analysis, including social networks, clustering, and
natural language processing.Comment: in Journal of Statistics Education, Volume 23, Number 2, 201
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely
qualitative, but with bibliometric data it is possible and desirable to develop
a quantitative picture of scientific progress. Furthermore it is also important
to allocate finite resources to research topics that have growth potential, to
accelerate the process from scientific breakthroughs to technological
innovations. In this paper, we address this problem of quantitative knowledge
evolution by analysing the APS publication data set from 1981 to 2010. We build
the bibliographic coupling and co-citation networks, use the Louvain method to
detect topical clusters (TCs) in each year, measure the similarity of TCs in
consecutive years, and visualize the results as alluvial diagrams. Having the
predictive features describing a given TC and its known evolution in the next
year, we can train a machine learning model to predict future changes of TCs,
i.e., their continuing, dissolving, merging and splitting. We found the number
of papers from certain journals, the degree, closeness, and betweenness to be
the most predictive features. Additionally, betweenness increases significantly
for merging events, and decreases significantly for splitting events. Our
results represent a first step from a descriptive understanding of the Science
of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include
Predicting Community Evolution in Social Networks
Nowadays, sustained development of different social media can be observed
worldwide. One of the relevant research domains intensively explored recently
is analysis of social communities existing in social media as well as
prediction of their future evolution taking into account collected historical
evolution chains. These evolution chains proposed in the paper contain group
states in the previous time frames and its historical transitions that were
identified using one out of two methods: Stable Group Changes Identification
(SGCI) and Group Evolution Discovery (GED). Based on the observed evolution
chains of various length, structural network features are extracted, validated
and selected as well as used to learn classification models. The experimental
studies were performed on three real datasets with different profile: DBLP,
Facebook and Polish blogosphere. The process of group prediction was analysed
with respect to different classifiers as well as various descriptive feature
sets extracted from evolution chains of different length. The results revealed
that, in general, the longer evolution chains the better predictive abilities
of the classification models. However, chains of length 3 to 7 enabled the
GED-based method to almost reach its maximum possible prediction quality. For
SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page
Absorbing Random Walks Interpolating Between Centrality Measures on Complex Networks
Centrality, which quantifies the "importance" of individual nodes, is among
the most essential concepts in modern network theory. As there are many ways in
which a node can be important, many different centrality measures are in use.
Here, we concentrate on versions of the common betweenness and it closeness
centralities. The former measures the fraction of paths between pairs of nodes
that go through a given node, while the latter measures an average inverse
distance between a particular node and all other nodes. Both centralities only
consider shortest paths (i.e., geodesics) between pairs of nodes. Here we
develop a method, based on absorbing Markov chains, that enables us to
continuously interpolate both of these centrality measures away from the
geodesic limit and toward a limit where no restriction is placed on the length
of the paths the walkers can explore. At this second limit, the interpolated
betweenness and closeness centralities reduce, respectively, to the well-known
it current betweenness and resistance closeness (information) centralities. The
method is tested numerically on four real networks, revealing complex changes
in node centrality rankings with respect to the value of the interpolation
parameter. Non-monotonic betweenness behaviors are found to characterize nodes
that lie close to inter-community boundaries in the studied networks
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