11,744 research outputs found
Fast Detection of Community Structures using Graph Traversal in Social Networks
Finding community structures in social networks is considered to be a
challenging task as many of the proposed algorithms are computationally
expensive and does not scale well for large graphs. Most of the community
detection algorithms proposed till date are unsuitable for applications that
would require detection of communities in real-time, especially for massive
networks. The Louvain method, which uses modularity maximization to detect
clusters, is usually considered to be one of the fastest community detection
algorithms even without any provable bound on its running time. We propose a
novel graph traversal-based community detection framework, which not only runs
faster than the Louvain method but also generates clusters of better quality
for most of the benchmark datasets. We show that our algorithms run in O(|V | +
|E|) time to create an initial cover before using modularity maximization to
get the final cover.
Keywords - community detection; Influenced Neighbor Score; brokers; community
nodes; communitiesComment: 29 pages, 9 tables, and 13 figures. Accepted in "Knowledge and
Information Systems", 201
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Abstractive community detection is an important spoken language understanding
task, whose goal is to group utterances in a conversation according to whether
they can be jointly summarized by a common abstractive sentence. This paper
provides a novel approach to this task. We first introduce a neural contextual
utterance encoder featuring three types of self-attention mechanisms. We then
train it using the siamese and triplet energy-based meta-architectures.
Experiments on the AMI corpus show that our system outperforms multiple
energy-based and non-energy based baselines from the state-of-the-art. Code and
data are publicly available.Comment: Update baseline
Modularity functions maximization with nonnegative relaxation facilitates community detection in networks
We show here that the problem of maximizing a family of quantitative
functions, encompassing both the modularity (Q-measure) and modularity density
(D-measure), for community detection can be uniformly understood as a
combinatoric optimization involving the trace of a matrix called modularity
Laplacian. Instead of using traditional spectral relaxation, we apply
additional nonnegative constraint into this graph clustering problem and design
efficient algorithms to optimize the new objective. With the explicit
nonnegative constraint, our solutions are very close to the ideal community
indicator matrix and can directly assign nodes into communities. The
near-orthogonal columns of the solution can be reformulated as the posterior
probability of corresponding node belonging to each community. Therefore, the
proposed method can be exploited to identify the fuzzy or overlapping
communities and thus facilitates the understanding of the intrinsic structure
of networks. Experimental results show that our new algorithm consistently,
sometimes significantly, outperforms the traditional spectral relaxation
approaches
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