54,640 research outputs found
Community Detection in Networks with Node Attributes
Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are
two possible sources of information one can use: the network structure, and the
features and attributes of nodes. Even though communities form around nodes
that have common edges and common attributes, typically, algorithms have only
focused on one of these two data modalities: community detection algorithms
traditionally focus only on the network structure, while clustering algorithms
mostly consider only node attributes. In this paper, we develop Communities
from Edge Structure and Node Attributes (CESNA), an accurate and scalable
algorithm for detecting overlapping communities in networks with node
attributes. CESNA statistically models the interaction between the network
structure and the node attributes, which leads to more accurate community
detection as well as improved robustness in the presence of noise in the
network structure. CESNA has a linear runtime in the network size and is able
to process networks an order of magnitude larger than comparable approaches.
Last, CESNA also helps with the interpretation of detected communities by
finding relevant node attributes for each community.Comment: Published in the proceedings of IEEE ICDM '1
Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems
Unveiling the community structure of networks is a powerful methodology to
comprehend interconnected systems across the social and natural sciences. To
identify different types of functional modules in interaction data aggregated
in a single network layer, researchers have developed many powerful methods.
For example, flow-based methods have proven useful for identifying modular
dynamics in weighted and directed networks that capture constraints on flow in
the systems they represent. However, many networked systems consist of agents
or components that exhibit multiple layers of interactions. Inevitably,
representing this intricate network of networks as a single aggregated network
leads to information loss and may obscure the actual organization. Here we
propose a method based on compression of network flows that can identify
modular flows in non-aggregated multilayer networks. Our numerical experiments
on synthetic networks show that the method can accurately identify modules that
cannot be identified in aggregated networks or by analyzing the layers
separately. We capitalize on our findings and reveal the community structure of
two multilayer collaboration networks: scientists affiliated to the Pierre
Auger Observatory and scientists publishing works on networks on the arXiv.
Compared to conventional aggregated methods, the multilayer method reveals
smaller modules with more overlap that better capture the actual organization
Detecting hierarchical and overlapping network communities using locally optimal modularity changes
Agglomerative clustering is a well established strategy for identifying
communities in networks. Communities are successively merged into larger
communities, coarsening a network of actors into a more manageable network of
communities. The order in which merges should occur is not in general clear,
necessitating heuristics for selecting pairs of communities to merge. We
describe a hierarchical clustering algorithm based on a local optimality
property. For each edge in the network, we associate the modularity change for
merging the communities it links. For each community vertex, we call the
preferred edge that edge for which the modularity change is maximal. When an
edge is preferred by both vertices that it links, it appears to be the optimal
choice from the local viewpoint. We use the locally optimal edges to define the
algorithm: simultaneously merge all pairs of communities that are connected by
locally optimal edges that would increase the modularity, redetermining the
locally optimal edges after each step and continuing so long as the modularity
can be further increased. We apply the algorithm to model and empirical
networks, demonstrating that it can efficiently produce high-quality community
solutions. We relate the performance and implementation details to the
structure of the resulting community hierarchies. We additionally consider a
complementary local clustering algorithm, describing how to identify
overlapping communities based on the local optimality condition.Comment: 10 pages; 4 tables, 3 figure
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
A similarity-based community detection method with multiple prototype representation
Communities are of great importance for understanding graph structures in
social networks. Some existing community detection algorithms use a single
prototype to represent each group. In real applications, this may not
adequately model the different types of communities and hence limits the
clustering performance on social networks. To address this problem, a
Similarity-based Multi-Prototype (SMP) community detection approach is proposed
in this paper. In SMP, vertices in each community carry various weights to
describe their degree of representativeness. This mechanism enables each
community to be represented by more than one node. The centrality of nodes is
used to calculate prototype weights, while similarity is utilized to guide us
to partitioning the graph. Experimental results on computer generated and
real-world networks clearly show that SMP performs well for detecting
communities. Moreover, the method could provide richer information for the
inner structure of the detected communities with the help of prototype weights
compared with the existing community detection models
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