Community detection in networks: Structural communities versus ground truth

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 (non-topological) 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 to be substantially modified, or that metadata groups may not be recoverable from topology alone.Comment: 21 pages, 19 figure

Characterizing the community structure of complex networks

Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as ``fingerprints'' of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Our findings are verified by the use of two fundamentally different community detection methods.Comment: 15 pages, 20 figures, 4 table

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

How far do network effects spill over? Evidence from an empirical study of performance differentials in interorganizational networks

Organizations join interorganizational networks in the hope of gaining exposure to learning opportunities, and accessing valuable extramural resources and knowledge. In this paper we argue that participation in interorganizational networks also reduces performance differentials among organizational nodes. We examine three alternative mechanisms capable of sustaining this prediction. The first (strength of ties) operates at a strictly local level defined in terms of dyadic relations linking organizations. The second mechanism (social proximity) operates at an intermediate – or meso level of interdependence defined in terms of membership in overlapping cliques into which interorganizational networks are typically organized. The third mechanism (structural equivalence) is global and pertains to jointly occupied network positions. The objective of this paper is to examine at which of these levels network effects operate to reduce performance differentials among members of interorganizational networks. Our empirical analysis of performance differentials between hospitals in a regional community supports the following conclusions: (i) performance spillover effects are highly differentiated and vary significantly across network levels; (ii) organizations occupying similar positions within the network are more similar in terms of performance; (iii) joint membership in multiple sub-groups (or cliques) reduces performance differentials up to a limit; after this limit is reached, the performance of organizational partners begins to diverge; (iv) the strength of direct collaboration between organizational partners does not necessarily reduce interorganizational performance differentials. The results of the study are new because available research on interorganizational networks says little about the range of network effects, i.e., about how far the performance spillover effects that operate through networks propagate throughout organizational fields and communities. These results are also consequential because they suggest that network effects on performance differentials are sensitive to the specification of network boundaries

Analysis of group evolution prediction in complex networks

In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well

A Connected World: Social Networks and Organizations

This Element synthesizes the current state of research on organizational social networks from its early foundations to contemporary debates. It highlights the characteristics that make the social network perspective distinctive in the organizational research landscape, including its emphasis on structure and outcomes. It covers the main theoretical developments and summarizes the research design questions that organizational researchers face when collecting and analyzing network data. Then, it discusses current debates ranging from agency and structure to network volatility and personality. Finally, the Element envisages future research directions on the role of brokerage for individuals and communities, network cognition, and the importance of past ties. Overall, the Element provides an innovative angle for understanding organizational social networks, engaging in empirical network research, and nurturing further theoretical development on the role of social interactions and connectedness in modern organizations