Community detection is a fundamental problem in social network analysis consisting , roughly speaking, in dividing social actors ( modelled as nodes in a social graph) with certain social connections ( modelled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes , although major real-world  networks provide additional actors' information such as age, gender, interests, etc. , traditionally called node attributes. It is known that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This has led to a relatively novel direction in community detection --- constructing algorithms that use both the structure and the attributes of the network (modelled already via a node-attributed graph) to yield more informative and qualitative results. During the last decade many  methods based on different ideas and techniques have appearedin this direction . Although there exist some partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause  uncertainty in practice. In this paper we aim at  clarifying the overall situation by proposing a clear classification of the methods and providing a comprehensive survey of the available results . We not only group and analyse the corresponding methods but also focus on practical aspects, including the information which methods outperform others and which datasets and quality measures are used for evaluation .
Community detection is a fundamental problem in social network analysis consisting in unsupervised dividing social actors ( nodes in a social graph) with certain social connections ( edges in a social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with network structure and ignore features of its nodes (called node attributes), although many real-world social networks provide additional actors' information such as interests. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of network (i.e. deal with a node-attributed graph) to yield more informative and qualitative results. During the last decade many such methods based on different ideas have appeared . Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice. In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how structure and attributes are fused . We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future .
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, roughly speaking, in
in unsupervised
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modelled as
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modelled as edges in the
edges in a
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the structure of the network
network structure
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the nodes , although major
its nodes (called node attributes), although many
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social
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age, gender, interests, etc. , traditionally called node attributes. It is known
interests. It is believed
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detected
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has led to a relatively novel direction in community detection --- constructing algorithms
belief has motivated the progress in developing community detection methods
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the network (modelled already via
network (i.e. deal with
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such
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and techniques have appearedin this direction
have appeared
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some
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repetitions in methodology and
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describing and
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by proposing a clear classification of the methods and providing a comprehensive survey of the available results
in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how structure and attributes are fused
R
group and analyse the corresponding methods but also focus on practical aspects, including the
give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available
R
evaluation
their evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future