16 research outputs found
DEMON: a Local-First Discovery Method for Overlapping Communities
Community discovery in complex networks is an interesting problem with a
number of applications, especially in the knowledge extraction task in social
and information networks. However, many large networks often lack a particular
community organization at a global level. In these cases, traditional graph
partitioning algorithms fail to let the latent knowledge embedded in modular
structure emerge, because they impose a top-down global view of a network. We
propose here a simple local-first approach to community discovery, able to
unveil the modular organization of real complex networks. This is achieved by
democratically letting each node vote for the communities it sees surrounding
it in its limited view of the global system, i.e. its ego neighborhood, using a
label propagation algorithm; finally, the local communities are merged into a
global collection. We tested this intuition against the state-of-the-art
overlapping and non-overlapping community discovery methods, and found that our
new method clearly outperforms the others in the quality of the obtained
communities, evaluated by using the extracted communities to predict the
metadata about the nodes of several real world networks. We also show how our
method is deterministic, fully incremental, and has a limited time complexity,
so that it can be used on web-scale real networks.Comment: 9 pages; Proceedings of the 18th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 201
Community Detection via Maximization of Modularity and Its Variants
In this paper, we first discuss the definition of modularity (Q) used as a
metric for community quality and then we review the modularity maximization
approaches which were used for community detection in the last decade. Then, we
discuss two opposite yet coexisting problems of modularity optimization: in
some cases, it tends to favor small communities over large ones while in
others, large communities over small ones (so called the resolution limit
problem). Next, we overview several community quality metrics proposed to solve
the resolution limit problem and discuss Modularity Density (Qds) which
simultaneously avoids the two problems of modularity. Finally, we introduce two
novel fine-tuned community detection algorithms that iteratively attempt to
improve the community quality measurements by splitting and merging the given
network community structure. The first of them, referred to as Fine-tuned Q, is
based on modularity (Q) while the second one is based on Modularity Density
(Qds) and denoted as Fine-tuned Qds. Then, we compare the greedy algorithm of
modularity maximization (denoted as Greedy Q), Fine-tuned Q, and Fine-tuned Qds
on four real networks, and also on the classical clique network and the LFR
benchmark networks, each of which is instantiated by a wide range of
parameters. The results indicate that Fine-tuned Qds is the most effective
among the three algorithms discussed. Moreover, we show that Fine-tuned Qds can
be applied to the communities detected by other algorithms to significantly
improve their results
Modeling the Product Space as a Network
In the market basket setting, we are given a series of transactions each
composed of one or more items and the goal is to find relationships
between items, usually sets of items that tend to occur in the same
transaction. Association rules, a popular approach for mining such data,
are limited in the ability to express complex interactions between
items. Our work defines some of these limitations and addresses them by
modeling the set of transactions as a network. We develop both a general
methodology for analyzing networks of products, and a privacy-preserving
protocol such that product network information can be securely shared
among stores. In general, our network based view of transactional data
is able to infer relationships that are more expressive and expansive
than those produced by a typical association rules analysis