83,334 research outputs found
Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament
In this paper, we want to study the informative value of negative links in
signed complex networks. For this purpose, we extract and analyze a collection
of signed networks representing voting sessions of the European Parliament
(EP). We first process some data collected by the VoteWatch Europe Website for
the whole 7 th term (2009-2014), by considering voting similarities between
Members of the EP to define weighted signed links. We then apply a selection of
community detection algorithms, designed to process only positive links, to
these data. We also apply Parallel Iterative Local Search (Parallel ILS), an
algorithm recently proposed to identify balanced partitions in signed networks.
Our results show that, contrary to the conclusions of a previous study focusing
on other data, the partitions detected by ignoring or considering the negative
links are indeed remarkably different for these networks. The relevance of
negative links for graph partitioning therefore is an open question which
should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Signed Network Modeling Based on Structural Balance Theory
The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174
Negative Link Prediction in Social Media
Signed network analysis has attracted increasing attention in recent years.
This is in part because research on signed network analysis suggests that
negative links have added value in the analytical process. A major impediment
in their effective use is that most social media sites do not enable users to
specify them explicitly. In other words, a gap exists between the importance of
negative links and their availability in real data sets. Therefore, it is
natural to explore whether one can predict negative links automatically from
the commonly available social network data. In this paper, we investigate the
novel problem of negative link prediction with only positive links and
content-centric interactions in social media. We make a number of important
observations about negative links, and propose a principled framework NeLP,
which can exploit positive links and content-centric interactions to predict
negative links. Our experimental results on real-world social networks
demonstrate that the proposed NeLP framework can accurately predict negative
links with positive links and content-centric interactions. Our detailed
experiments also illustrate the relative importance of various factors to the
effectiveness of the proposed framework
Signed Link Analysis in Social Media Networks
Numerous real-world relations can be represented by signed networks with
positive links (e.g., trust) and negative links (e.g., distrust). Link analysis
plays a crucial role in understanding the link formation and can advance
various tasks in social network analysis such as link prediction. The majority
of existing works on link analysis have focused on unsigned social networks.
The existence of negative links determines that properties and principles of
signed networks are substantially distinct from those of unsigned networks,
thus we need dedicated efforts on link analysis in signed social networks. In
this paper, following social theories in link analysis in unsigned networks, we
adopt three social science theories, namely Emotional Information, Diffusion of
Innovations and Individual Personality, to guide the task of link analysis in
signed networks.Comment: In the 10th International AAAI Conference on Web and Social Media
(ICWSM-16
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