95,926 research outputs found
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Several natural phenomena and complex systems are often represented as
networks. Discovering their community structure is a fundamental task for
understanding these networks. Many algorithms have been proposed, but recently,
Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing
this task.In this paper, we introduce a simple, efficient, and
clustering-oriented model based on unsupervised \textbf{G}raph Attention
\textbf{A}uto\textbf{E}ncoder for community detection in attributed networks
(GAECO). The proposed model adeptly learns representations from both the
network's topology and attribute information, simultaneously addressing dual
objectives: reconstruction and community discovery. It places a particular
emphasis on discovering compact communities by robustly minimizing clustering
errors. The model employs k-means as an objective function and utilizes a
multi-head Graph Attention Auto-Encoder for decoding the representations.
Experiments conducted on three datasets of attributed networks show that our
method surpasses state-of-the-art algorithms in terms of NMI and ARI.
Additionally, our approach scales effectively with the size of the network,
making it suitable for large-scale applications. The implications of our
findings extend beyond biological network interpretation and social network
analysis, where knowledge of the fundamental community structure is essential.Comment: 7 pages, 5 Figure
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Multi-objective community detection applied to social and COVID-19 constructed networks
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks.
After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics.
For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network.
Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%
Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks
Networks have become a common data mining tool to encode relational definitions between a set of entities. Whether studying biological correlations, or communication between individuals in a social network, network analysis tools enable interpretation, prediction, and visualization of patterns in the data. Community detection is a well-developed subfield of network analysis, where the objective is to cluster nodes into 'communities' based on their connectivity patterns. There are many useful and robust approaches for identifying communities in a single, moderately-sized network, but the ability to work with more complicated types of networks containing extra or a large amount of information poses challenges. In this thesis, we address three types of challenging network data and how to adapt standard community detection approaches to handle these situations. In particular, we focus on networks that are large, attributed, and multilayer. First, we present a method for identifying communities in multilayer networks, where there exist multiple relational definitions between a set of nodes. Next, we provide a pre-processing technique for reducing the size of large networks, where standard community detection approaches might have inconsistent results or be prohibitively slow. We then introduce an extension to a probabilistic model for community structure to take into account node attribute information and develop a test to quantify the extent to which connectivity and attribute information align. Finally, we demonstrate example applications of these methods in biological and social networks. This work helps to advance the understand of network clustering, network compression, and the joint modeling of node attributes and network connectivity.Doctor of Philosoph
A Method for Characterizing Communities in Dynamic Attributed Complex Networks
Many methods have been proposed to detect communities, not only in plain, but
also in attributed, directed or even dynamic complex networks. In its simplest
form, a community structure takes the form of a partition of the node set. From
the modeling point of view, to be of some utility, this partition must then be
characterized relatively to the properties of the studied system. However, if
most of the existing works focus on defining methods for the detection of
communities, only very few try to tackle this interpretation problem. Moreover,
the existing approaches are limited either in the type of data they handle, or
by the nature of the results they output. In this work, we propose a method to
efficiently support such a characterization task. We first define a
sequence-based representation of networks, combining temporal information,
topological measures, and nodal attributes. We then describe how to identify
the most emerging sequential patterns of this dataset, and use them to
characterize the communities. We also show how to detect unusual behavior in a
community, and highlight outliers. Finally, as an illustration, we apply our
method to a network of scientific collaborations.Comment: IEEE/ACM International Conference on Advances in Social Network
Analysis and Mining (ASONAM), P\'ekin : China (2014
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
The Advantage of Evidential Attributes in Social Networks
Nowadays, there are many approaches designed for the task of detecting
communities in social networks. Among them, some methods only consider the
topological graph structure, while others take use of both the graph structure
and the node attributes. In real-world networks, there are many uncertain and
noisy attributes in the graph. In this paper, we will present how we detect
communities in graphs with uncertain attributes in the first step. The
numerical, probabilistic as well as evidential attributes are generated
according to the graph structure. In the second step, some noise will be added
to the attributes. We perform experiments on graphs with different types of
attributes and compare the detection results in terms of the Normalized Mutual
Information (NMI) values. The experimental results show that the clustering
with evidential attributes gives better results comparing to those with
probabilistic and numerical attributes. This illustrates the advantages of
evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
Debiasing Community Detection: The Importance of Lowly-Connected Nodes
Community detection is an important task in social network analysis, allowing
us to identify and understand the communities within the social structures.
However, many community detection approaches either fail to assign low degree
(or lowly-connected) users to communities, or assign them to trivially small
communities that prevent them from being included in analysis. In this work, we
investigate how excluding these users can bias analysis results. We then
introduce an approach that is more inclusive for lowly-connected users by
incorporating them into larger groups. Experiments show that our approach
outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity
scores while reducing the bias towards low-degree users
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