  Recently, considerable attention has been devoted to the prediction problems
arising from heterogeneous information networks. In this paper, we present a
new prediction task, Neighbor Distribution Prediction (NDP), which aims at
predicting the distribution of the labels on neighbors of a given node and is
valuable for many different applications in heterogeneous information networks.
The challenges of NDP mainly come from three aspects: the infinity of the state
space of a neighbor distribution, the sparsity of available data, and how to
fairly evaluate the predictions. To address these challenges, we first propose
an Evolution Factor Model (EFM) for NDP, which utilizes two new structures
proposed in this paper, i.e. Neighbor Distribution Vector (NDV) to represent
the state of a given node's neighbors, and Neighbor Label Evolution Matrix
(NLEM) to capture the dynamics of a neighbor distribution, respectively. We
further propose a learning algorithm for Evolution Factor Model. To overcome
the problem of data sparsity, the learning algorithm first clusters all the
nodes and learns an NLEM for each cluster instead of for each node. For fairly
evaluating the predicting results, we propose a new metric: Virtual Accuracy
(VA), which takes into consideration both the absolute accuracy and the
predictability of a node. Extensive experiments conducted on three real
datasets from different domains validate the effectiveness of our proposed
model EFM and metric VA.
