1,273 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 graph embedding: when everybody can sit closer to friends than enemies
Signed graphs are graphs with signed edges. They are commonly used to
represent positive and negative relationships in social networks. While balance
theory and clusterizable graphs deal with signed graphs to represent social
interactions, recent empirical studies have proved that they fail to reflect
some current practices in real social networks. In this paper we address the
issue of drawing signed graphs and capturing such social interactions. We relax
the previous assumptions to define a drawing as a model in which every vertex
has to be placed closer to its neighbors connected via a positive edge than its
neighbors connected via a negative edge in the resulting space. Based on this
definition, we address the problem of deciding whether a given signed graph has
a drawing in a given -dimensional Euclidean space. We present forbidden
patterns for signed graphs that admit the introduced definition of drawing in
the Euclidean plane and line. We then focus on the -dimensional case, where
we provide a polynomial time algorithm that decides if a given complete signed
graph has a drawing, and constructs it when applicable
Graph Sample and Hold: A Framework for Big-Graph Analytics
Sampling is a standard approach in big-graph analytics; the goal is to
efficiently estimate the graph properties by consulting a sample of the whole
population. A perfect sample is assumed to mirror every property of the whole
population. Unfortunately, such a perfect sample is hard to collect in complex
populations such as graphs (e.g. web graphs, social networks etc), where an
underlying network connects the units of the population. Therefore, a good
sample will be representative in the sense that graph properties of interest
can be estimated with a known degree of accuracy. While previous work focused
particularly on sampling schemes used to estimate certain graph properties
(e.g. triangle count), much less is known for the case when we need to estimate
various graph properties with the same sampling scheme. In this paper, we
propose a generic stream sampling framework for big-graph analytics, called
Graph Sample and Hold (gSH). To begin, the proposed framework samples from
massive graphs sequentially in a single pass, one edge at a time, while
maintaining a small state. We then show how to produce unbiased estimators for
various graph properties from the sample. Given that the graph analysis
algorithms will run on a sample instead of the whole population, the runtime
complexity of these algorithm is kept under control. Moreover, given that the
estimators of graph properties are unbiased, the approximation error is kept
under control. Finally, we show the performance of the proposed framework (gSH)
on various types of graphs, such as social graphs, among others
Balanced Butterfly Counting in Bipartite-Network
Bipartite graphs offer a powerful framework for modeling complex
relationships between two distinct types of vertices, incorporating
probabilistic, temporal, and rating-based information. While the research
community has extensively explored various types of bipartite relationships,
there has been a notable gap in studying Signed Bipartite Graphs, which capture
liking / disliking interactions in real-world networks such as
customer-rating-product and senator-vote-bill. Balance butterflies,
representing 2 x 2 bicliques, provide crucial insights into antagonistic
groups, balance theory, and fraud detection by leveraging the signed
information. However, such applications require counting balance butterflies
which remains unexplored. In this paper, we propose a new problem: counting
balance butterflies in a signed bipartite graph. To address this problem, we
adopt state-of-the-art algorithms for butterfly counting, establishing a smart
baseline that reduces the time complexity for solving our specific problem. We
further introduce a novel bucket approach specifically designed to count
balanced butterflies efficiently. We propose a parallelized version of the
bucketing approach to enhance performance. Extensive experimental studies on
nine real-world datasets demonstrate that our proposed bucket-based algorithm
is up to 120x faster over the baseline, and the parallel implementation of the
bucket-based algorithm is up to 45x faster over the single core execution.
Moreover, a real-world case study showcases the practical application and
relevance of counting balanced butterflies
- …