97,907 research outputs found
Leveraging Node Attributes for Incomplete Relational Data
Relational data are usually highly incomplete in practice, which inspires us
to leverage side information to improve the performance of community detection
and link prediction. This paper presents a Bayesian probabilistic approach that
incorporates various kinds of node attributes encoded in binary form in
relational models with Poisson likelihood. Our method works flexibly with both
directed and undirected relational networks. The inference can be done by
efficient Gibbs sampling which leverages sparsity of both networks and node
attributes. Extensive experiments show that our models achieve the
state-of-the-art link prediction results, especially with highly incomplete
relational data.Comment: Appearing in ICML 201
A Novel Local Community Detection Method Using Evolutionary Computation.
The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained
An optimisation tool for robust community detection algorithms using content and topology information
With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Community detection by label propagation with compression of flow
The label propagation algorithm (LPA) has been proved to be a fast and
effective method for detecting communities in large complex networks. However,
its performance is subject to the non-stable and trivial solutions of the
problem. In this paper, we propose a modified label propagation algorithm LPAf
to efficiently detect community structures in networks. Instead of the majority
voting rule of the basic LPA, LPAf updates the label of a node by considering
the compression of a description of random walks on a network. A multi-step
greedy agglomerative strategy is employed to enable LPAf to escape the local
optimum. Furthermore, an incomplete update condition is also adopted to speed
up the convergence. Experimental results on both synthetic and real-world
networks confirm the effectiveness of our algorithm
CitNetExplorer: A new software tool for analyzing and visualizing citation networks
We present CitNetExplorer, a new software tool for analyzing and visualizing
citation networks of scientific publications. CitNetExplorer can for instance
be used to study the development of a research field, to delineate the
literature on a research topic, and to support literature reviewing. We first
introduce the main concepts that need to be understood when working with
CitNetExplorer. We then demonstrate CitNetExplorer by using the tool to analyze
the scientometric literature and the literature on community detection in
networks. Finally, we discuss some technical details on the construction,
visualization, and analysis of citation networks in CitNetExplorer
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