Real-world networks are known to exhibit community structure, characterized by presence of dense node clusters with loose edge connections among them. Although identification of communities is a well-studied subject, most approaches only focus on edge-based criteria which may not incorporate important grouping information captured by higher-order structures e.g., cliques and cycles, to name a few. In order to overcome this limitation, the present paper advocates a novel three-way tensor network representation that captures spatial dependencies among node neighborhoods. Each tensor slice captures a connectivity matrix pertaining to a unique egonet, defined as the subgraph induced by a node and its single-hop neighbors. Constrained tensor factorization is pursued to reveal the hidden and possibly overlapping community structure. Numerical tests on synthetic and real world networks corroborate the efficacy of the novel approach.