21,725 research outputs found
Viewpoint Discovery and Understanding in Social Networks
The Web has evolved to a dominant platform where everyone has the opportunity
to express their opinions, to interact with other users, and to debate on
emerging events happening around the world. On the one hand, this has enabled
the presence of different viewpoints and opinions about a - usually
controversial - topic (like Brexit), but at the same time, it has led to
phenomena like media bias, echo chambers and filter bubbles, where users are
exposed to only one point of view on the same topic. Therefore, there is the
need for methods that are able to detect and explain the different viewpoints.
In this paper, we propose a graph partitioning method that exploits social
interactions to enable the discovery of different communities (representing
different viewpoints) discussing about a controversial topic in a social
network like Twitter. To explain the discovered viewpoints, we describe a
method, called Iterative Rank Difference (IRD), which allows detecting
descriptive terms that characterize the different viewpoints as well as
understanding how a specific term is related to a viewpoint (by detecting other
related descriptive terms). The results of an experimental evaluation showed
that our approach outperforms state-of-the-art methods on viewpoint discovery,
while a qualitative analysis of the proposed IRD method on three different
controversial topics showed that IRD provides comprehensive and deep
representations of the different viewpoints
Tackling information asymmetry in networks: a new entropy-based ranking index
Information is a valuable asset for agents in socio-economic systems, a
significant part of the information being entailed into the very network of
connections between agents. The different interlinkages patterns that agents
establish may, in fact, lead to asymmetries in the knowledge of the network
structure; since this entails a different ability of quantifying relevant
systemic properties (e.g. the risk of financial contagion in a network of
liabilities), agents capable of providing a better estimate of (otherwise)
unaccessible network properties, ultimately have a competitive advantage. In
this paper, we address for the first time the issue of quantifying the
information asymmetry arising from the network topology. To this aim, we define
a novel index - InfoRank - intended to measure the quality of the information
possessed by each node, computing the Shannon entropy of the ensemble
conditioned on the node-specific information. Further, we test the performance
of our novel ranking procedure in terms of the reconstruction accuracy of the
(unaccessible) network structure and show that it outperforms other popular
centrality measures in identifying the "most informative" nodes. Finally, we
discuss the socio-economic implications of network information asymmetry.Comment: 12 pages, 8 figure
A Bag-of-Paths Node Criticality Measure
This work compares several node (and network) criticality measures
quantifying to which extend each node is critical with respect to the
communication flow between nodes of the network, and introduces a new measure
based on the Bag-of-Paths (BoP) framework. Network disconnection simulation
experiments show that the new BoP measure outperforms all the other measures on
a sample of Erdos-Renyi and Albert-Barabasi graphs. Furthermore, a faster
(still O(n^3)), approximate, BoP criticality relying on the Sherman-Morrison
rank-one update of a matrix is introduced for tackling larger networks. This
approximate measure shows similar performances as the original, exact, one
A multi-class approach for ranking graph nodes: models and experiments with incomplete data
After the phenomenal success of the PageRank algorithm, many researchers have
extended the PageRank approach to ranking graphs with richer structures beside
the simple linkage structure. In some scenarios we have to deal with
multi-parameters data where each node has additional features and there are
relationships between such features.
This paper stems from the need of a systematic approach when dealing with
multi-parameter data. We propose models and ranking algorithms which can be
used with little adjustments for a large variety of networks (bibliographic
data, patent data, twitter and social data, healthcare data). In this paper we
focus on several aspects which have not been addressed in the literature: (1)
we propose different models for ranking multi-parameters data and a class of
numerical algorithms for efficiently computing the ranking score of such
models, (2) by analyzing the stability and convergence properties of the
numerical schemes we tune a fast and stable technique for the ranking problem,
(3) we consider the issue of the robustness of our models when data are
incomplete. The comparison of the rank on the incomplete data with the rank on
the full structure shows that our models compute consistent rankings whose
correlation is up to 60% when just 10% of the links of the attributes are
maintained suggesting the suitability of our model also when the data are
incomplete
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Non-Conservative Diffusion and its Application to Social Network Analysis
The random walk is fundamental to modeling dynamic processes on networks.
Metrics based on the random walk have been used in many applications from image
processing to Web page ranking. However, how appropriate are random walks to
modeling and analyzing social networks? We argue that unlike a random walk,
which conserves the quantity diffusing on a network, many interesting social
phenomena, such as the spread of information or disease on a social network,
are fundamentally non-conservative. When an individual infects her neighbor
with a virus, the total amount of infection increases. We classify diffusion
processes as conservative and non-conservative and show how these differences
impact the choice of metrics used for network analysis, as well as our
understanding of network structure and behavior. We show that Alpha-Centrality,
which mathematically describes non-conservative diffusion, leads to new
insights into the behavior of spreading processes on networks. We give a
scalable approximate algorithm for computing the Alpha-Centrality in a massive
graph. We validate our approach on real-world online social networks of Digg.
We show that a non-conservative metric, such as Alpha-Centrality, produces
better agreement with empirical measure of influence than conservative metrics,
such as PageRank. We hope that our investigation will inspire further
exploration into the realms of conservative and non-conservative metrics in
social network analysis
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