A0831
Title: Individual-centered partial information in social networks
Authors: Xiao Han - University of Science and Technology of China (China) [presenting]
Abstract: In statistical network modeling and inference, we often assume either the full network is available or multiple subgraphs can be sampled to estimate various global properties of the full network. However, in a real social network, people frequently make decisions based on their local view of the network alone. We consider a partial information framework that characterizes the local network centered at a given individual by path length (or knowledge depth $L$) and gives rise to a partial adjacency matrix. Under $L=2$, we focus on the problem of (global) community detection using the popular stochastic block model (SBM) and its degree-corrected variant (DCSBM). We derive general properties of the eigenvalues and eigenvectors from the major term of the partial adjacency matrix and propose new spectral-based community detection algorithms for these two types of models, which can achieve almost exact recovery under appropriate conditions. Our settings in the DCSBMalso allow us to interpret the efficiency of clustering using neighborhood features of the central node. Using simulated and real networks, we demonstrate the performance of our algorithms in inferring global community memberships using a partial network. In particular, we show that the clustering accuracy indicates the different global structure is visible to different individuals.
