Finding meaningful communities - subnetworks of interest within a large scale network - is a problem with a variety of applications. Most existing work towards community detection focuses on a single network. However, many real-life applications naturally yield what we refer to as Triple Networks. Triple Networks are comprised of two networks, and the network of bipartite connections between their nodes. In this paper, we formulate and investigate the problem of finding Connected-Dense-Connected subgraph (CDC), a subnetwork which has the largest density in the bipartite network and whose sets of end points within each network induce connected subnetworks. These patterns represent communities based on the bipartite association between the networks. To our knowledge, such patterns cannot be detected by existing algorithms for a single network or heterogeneous networks. We show that finding CDC subgraphs is NP-hard and develop novel heuristics to obtain feasible solutions, the fastest of which is O(nlogn+m) with n nodes and m edges. We also study different variations of the CDC subgraphs. We perform experiments on a variety of real and synthetic Triple Networks to evaluate the effectiveness and efficiency of the developed methods. Employing these heuristics, we demonstrate how to identify communities of similar opinions and research interests, and factors influencing communities.