  Community is a fundamental and critical characteristic of an undirected
social network, making community detection be a vital yet thorny issue in
network representation learning. A symmetric and non-negative matrix
factorization (SNMF) model is frequently adopted to address this issue owing to
its great interpretability and scalability. However, it adopts a single latent
factor matrix to represent an undirected network for precisely representing its
symmetry, which leads to loss of representation learning ability due to the
reduced latent space. Motivated by this discovery, this paper proposes a novel
Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS)
model that adopts three-fold ideas: a) Representing a target undirected network
with multiple latent factor matrices, thus preserving its representation
learning capacity; b) Incorporating a symmetry-regularizer that preserves the
symmetry of the learnt low-rank approximation to the adjacency matrix into the
loss function, thus making the resultant detector well-aware of the target
network's symmetry; and c) Introducing a graph-regularizer that preserves local
invariance of the network's intrinsic geometry, thus making the achieved
detector well-aware of community structure within the target network.
Extensively empirical studies on eight real-world social networks from
industrial applications demonstrate that the proposed CFS model significantly
outperforms state-of-the-art models in achieving highly-accurate community
detection results.
