Community detection or clustering is crucial for understanding the structure of complex systems. In some networks, nodes are allowed to be linked by either 'positive' or 'negative' edges. Such networks are called signed networks. Discovering communities in signed networks is more challenging  . In this article , we innovatively propose a non-backtracking matrix for signed networks, and theoretically derive a detectability threshold and prove the feasibility in community detection. Furthermore, we improve the operator by considering the balanced paths in the network ( denoted as balanced non-backtracking operator ). Simulation results demonstrate that the balanced non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based and the signed non-backtracking matrix-based algorithm. It shows great potential to detect communities with or without overlap.
Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either "positive" or "negative" edges; such networks are called signed networks. Discovering communities in signed networks is more challenging than that in unsigned networks . In this study , we innovatively develop a non-backtracking matrix of signed networks, theoretically derive a detectability threshold for this matrix, and demonstrate the feasibility of using the matrix for community detection. We further improve the developed matrix by considering the balanced paths in the network ( referred to as a balanced non-backtracking matrix ). Simulation results demonstrate that the algorithm based on the balanced nonbacktracking matrix significantly outperforms those based on the adjacency matrix and the signed non-backtracking matrix. The proposed (improved) matrix shows great potential for detecting communities with or without overlap.
R
crucial
a crucial task
R
allowed
permitted
R
'positive' or 'negative' edges. Such
"positive" or "negative" edges; such
A
than that in unsigned networks
R
article
study
R
propose
develop
R
for
of
D
and
R
and prove the feasibility in
for this matrix, and demonstrate the feasibility of using the matrix for
R
Furthermore, we improve the operator
We further improve the developed matrix
R
denoted as
referred to as a
R
operator
matrix
R
balanced non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based
algorithm based on the balanced nonbacktracking matrix significantly outperforms those based on the adjacency matrix
R
matrix-based algorithm. It
matrix. The proposed (improved) matrix
R
to detect
for detecting