Extracting Dense and Connected Communities in Dual Networks: An Alignment Based Algorithm

Abstract

Networks-based models have been used to represent and analyse datasets in many fields such as computational biology, medical informatics and social networks. Nevertheless, it has been recently shown that, in their standard form, they are unable to capture some aspects of the investigated scenarios. Thus, more complex and enriched models, such as heterogeneous networks or dual networks, have been proposed. We focus on the latter model, which consists of a pair of networks having the same nodes but different edges. In dual networks, one network, called physical, has unweighted edges representing binary associations among nodes. The other is an edge-weighted one where weights represent the strength of the associations among nodes. Dual networks capture in a single model some aspects that cannot be described by using a standard model. Dual networks can be used, for instance, to capture a co-authorships network, where physical network represents co-authors. In contrast, the conceptual network is used to model topics sharing among a couple of authors by means of edge connections. This allows capturing similar interests among authors even though they are not co-authors. We propose an innovative algorithm to find the Densest Connected Subgraph (DCS) in dual networks. DCS is the largest density subgraph in the conceptual network, which is also connected in the physical network. A DCS represents a set of highly similar nodes. Moreover, since DCS is a computationally hard problem, we propose novel heuristics to solve it. We tested the proposed algorithm on social, biological, and co-authorship networks. Results demonstrate that our approach is efficient and is able to extract meaningful information from dual networks. INDEX TERMS DCS, Dual Networks, graph alignment, social networks. Recently, more complex models have also been introduced. The use of a pair of graphs representing two different views of the same scenario has been introduced to detect hidden properties which are not detected by single network-based VOLUME 4, 2016

Using dual-network-analyser for communities detecting in dual networks

“…4 Architecture of the Dual Network Analyser tool

Fig. 5 Alignment Example: the Algorithm receives as input two networks and a set of similarity relationship among nodes of the networks (dashed lines) [ 18 ]

Fig. 6 First, the algorithm builds the nodes of the heterogeneous alignment graph.

…”

“… Alignment Example: the Algorithm receives as input two networks and a set of similarity relationship among nodes of the networks (dashed lines) [ 18 ] …”

“…For instance DNs are used when modelling physical and conceptual interactions, i.e. mapping two kinds of relations [ 18 – 23 ].…”

“…Adopting a DN to model real scenarios allows us to study interesting network properties using graph theory algorithms. For example, a Densest Connected Subgraph (DCS) [ 14 ] and [ 18 ]) may represent a set of related users of a social network, not necessarily connected. In a recommender system, a Densest Connected Subgraph (DCS) in a DN represents a set of nodes closely related to the conceptual network and connected to the physical one [ 14 ].…”

A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks

“…For instance (Guzzi et al. 2020 ), showed that DCS may suggest missing links in social networks, capture similar interests among authors in a co-authorships dual network, where physical network represents co-authors and the conceptual network is used to model topics shared.…”