Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has t...
Community detection is an important part of network analysis and has become a very popular field of ...
Empirical analysis of network data has been widely conducted for understanding and predicting the st...
An important problem in the analysis of network data is the detection of groups of densely interconn...
Algorithms to find communities in networks rely just on structural information and search for cohesi...
Across many scientific domains, there is a common need to automatically extract a simplified view or...
Recently, it was recognized that the problems lying between the order and chaos require a new scient...
International audienceCommunity detection emerged as an important exploratory task in complex networ...
Community structure is a network characteristic where nodes can be naturally divided into densely co...
BACKGROUND: Community structure is one of the key properties of complex networks and plays a crucial...
Background Community structure is one of the key properties of complex networks and plays a crucial ...
International audienceCommunity structure is of paramount importance for the understanding of comple...
Community detection, the decomposition of a graph into essential building blocks, has been a core re...
Abstract. Community detection is the process of assigning nodes and links in significant communities...
Within the broad area of social network analysis research, the study of communities has become an im...
Network methods have had profound influence in many domains and disciplines in the past decade. Comm...
Community detection is an important part of network analysis and has become a very popular field of ...
Empirical analysis of network data has been widely conducted for understanding and predicting the st...
An important problem in the analysis of network data is the detection of groups of densely interconn...
Algorithms to find communities in networks rely just on structural information and search for cohesi...
Across many scientific domains, there is a common need to automatically extract a simplified view or...
Recently, it was recognized that the problems lying between the order and chaos require a new scient...
International audienceCommunity detection emerged as an important exploratory task in complex networ...
Community structure is a network characteristic where nodes can be naturally divided into densely co...
BACKGROUND: Community structure is one of the key properties of complex networks and plays a crucial...
Background Community structure is one of the key properties of complex networks and plays a crucial ...
International audienceCommunity structure is of paramount importance for the understanding of comple...
Community detection, the decomposition of a graph into essential building blocks, has been a core re...
Abstract. Community detection is the process of assigning nodes and links in significant communities...
Within the broad area of social network analysis research, the study of communities has become an im...
Network methods have had profound influence in many domains and disciplines in the past decade. Comm...
Community detection is an important part of network analysis and has become a very popular field of ...
Empirical analysis of network data has been widely conducted for understanding and predicting the st...
An important problem in the analysis of network data is the detection of groups of densely interconn...