In the last two decades, we are witnessing a huge increase of valuable big data structured in
the form of graphs or networks. To apply traditional machine learning and data analytic …

Considering Autocorrelation in Predictive Models 1 Introduction 2 Methods and evaluation
Page 1 Informatica 37 (2013) 107–108 107 Considering Autocorrelation in Predictive …

In this paper, we study the problem of performing multi-label classification on networked
data, where each instance in the network is assigned with multiple labels and the …

Social media such as blogs, Facebook, Twitter, YouTube and Flickr enables people of all
walks of life to express their thoughts, voice their opinions, and connect to each other more …

现有的链路预测方法的数据来源主要是基于邻居, 路径和随机游走的方法, 使用的是节点相似性
假设或者最大似然估计, 尚缺少基于神经网络的链路预测研究. 基于神经网络的一些研究表明 …

Given a graph, how can we automatically discover roles for nodes? Roles could be,
eg.,'bridges', or'peripherynodes', etc. Roles are compact summaries of a node's behavior …

Within-network regression addresses the task of regression in partially labeled networked
data where labels are sparse and continuous. Data for inference consist of entities …

Node attribute prediction tasks arise in a wide range of classification tasks on social
networks. Examples include detecting spam accounts, identifying compromised accounts …

We present a theoretical analysis framework for relational ensemble models. We show that
ensembles of collective classifiers can improve predictions for graph data by reducing errors …

The problem of collective classification (CC) for large-scale network data has received
considerable attention in the last decade. Enabling CC usually increases accuracy when …