This paper presents an algorithm and a tool for discovering scientific communities. Several approaches have been proposed to discover community structure applying clustering methods over different networks, such as co-authorship and citation networks. However, most existing approaches do not allow for overlapping of communities, which is instead natural when we consider communities of scientist...

UAVs are becoming standard platforms for applications aiming at photogrammetric data capture. Since these systems can be completely built-up at very reasonable prices, their use can be very cost effective. This is especially true while aiming at large scale aerial mapping of areas at limited extent. In principle, the photogrammetric evaluation of UAV-based imagery is feasible by of-theshelf com...

Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, the...

One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social network...

In a very general context, communities in networks are defined as groups of nodes that have some common properties such that connections are stronger between the nodes in a community than with the nodes in the rest of the network. It is quite common for nodes to participate in multiple communities. Therefore a community detection algorithm for such applications should be able to detect overlapp...

Chandelier (or axo-axonic) cells are a distinct group of GABAergic interneurons that innervate the axon initial segments of pyramidal cells and thus could have an important role controlling the activity of cortical circuits. To understand their connectivity, we labeled upper layers chandelier cells (ChCs) from mouse neocortex with a genetic strategy and studied how their axons contact local pop...

Communities in social networks are often defined as groups of densely connected actors. However, members of the same dense group are not equal but may differ largely in their social position or in the role they play. Furthermore, the same positions can be found across the borders of dense communities so that networks contain a significant group structure which does not coincide with the structu...

Introduction With the growth of social media, social network analysis draws a great attention and becomes a hot research topic in the field of complex network, web mining, information retrieval, etc. An important aspect of social networks analysis is community structure (Newman, 2003). In general, community detection methods are classified into two categories: overlapping methods (and non-overl...

Folksonomies like Delicious and LastFm are modeled as multilateral (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple relevant interests and same resour...

We previously looked at communities as simply dense subgraphs graphs, or as subgraphs such that each node has a large fraction of its edges inside. Another view, taken in [3], starts from the hubs and authorities model. It argues that a structure of densely linked hubs and authorities is a common feature of communities. Such a core, a dense bipartite graph, can be considered the “signature” of ...