标题： 预测异构信息网络中的邻居分布 显示英文标题

标题： Predicting Neighbor Distribution in Heterogeneous Information Networks

摘要： 最近，人们对来自异构信息网络的预测问题给予了相当多的关注。 在本文中，我们提出了一种新的预测任务，邻居分布预测（NDP），其目的是预测给定节点邻居上的标签分布，并且在异构信息网络中的许多不同应用中都有价值。 NDP的挑战主要来自三个方面：邻居分布的状态空间的无限性、可用数据的稀疏性以及如何公平地评估预测结果。 为了解决这些挑战，我们首先为NDP提出了一种进化因子模型（EFM），该模型利用了本文提出的两种新结构，即 邻居分布向量（NDV）来表示给定节点邻居的状态，以及邻居标签演化矩阵（NLEM）来分别捕捉邻居分布的动力学特性。 我们进一步提出了一个进化因子模型的学习算法。 为了克服数据稀疏性问题，学习算法首先对所有节点进行聚类，并为每个聚类而不是每个节点学习一个NLEM。 为了公平地评估预测结果，我们提出了一种新的度量标准：虚拟准确率（VA），该度量标准同时考虑了节点的绝对准确性和可预测性。 在三个来自不同领域的实际数据集上进行的大量实验验证了我们提出的模型EFM和度量标准VA的有效性。 显示英文摘要

摘要： 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.