Arrow Research search

Author name cluster

John Moore

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
1 author row

Possible papers

3

AAAI Conference 2017 Conference Paper

Deep Collective Inference

  • John Moore
  • Jennifer Neville

Collective inference is widely used to improve classification in network datasets. However, despite recent advances in deep learning and the successes of recurrent neural networks (RNNs), researchers have only just recently begun to study how to apply RNNs to heterogeneous graph and network datasets. There has been recent work on using RNNs for unsupervised learning in networks (e. g. , graph clustering, node embedding) and for prediction (e. g. , link prediction, graph classification), but there has been little work on using RNNs for node-based relational classification tasks. In this paper, we provide an end-to-end learning framework using RNNs for collective inference. Our main insight is to transform a node and its set of neighbors into an unordered sequence (of varying length) and use an LSTM-based RNN to predict the class label as the output of that sequence. We develop a collective inference method, which we refer to as Deep Collective Inference (DCI), that uses semi-supervised learning in partially-labeled networks and two label distribution correction mechanisms for imbalanced classes. We compare to several alternative methods on seven network datasets. DCI achieves up to a 12% reduction in error compared to the best alternative and a 25% reduction in error on average— over all methods, for all label proportions.

PDF Details

AAAI Conference 2015 Conference Paper

Incorporating Assortativity and Degree Dependence into Scalable Network Models

  • Stephen Mussmann
  • John Moore
  • Joseph Pfeiffer
  • Jennifer Neville

Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable generative graph models focus on modeling distributions of graphs that match real world network properties and scale to large datasets. Much work has focused on modeling networks with a power law degree distribution, clustering, and small diameter. In network analysis, the assortativity statistic is defined as the correlation between the degrees of linked nodes in the network. The assortativity measure can distinguish between types of networks—social networks commonly exhibit positive assortativity, in contrast to biological or technological networks that are typically disassortative. Despite this, little work has focused on scalable graph models that capture assortativity in networks. The contributions of our work are twofold. First, we prove that an unbounded number of pairs of networks exist with the same degree distribution and assortativity, yet different joint degree distributions. Thus, assortativity as a network measure cannot distinguish between graphs with complex (nonlinear) dependence in their joint degree distributions. Motivated by this finding, we introduce a generative graph model that explicitly estimates and models the joint degree distribution. Our Binned Chung Lu method accurately captures both the joint degree distribution and assortativity, while still matching characteristics such as the degree distribution and clustering coefficients. Further, our method has subquadratic learning and sampling methods that enable scaling to large, real world networks. We evaluate performance compared to other scalable graph models on six real world networks, including a citation network with over 14 million edges.

PDF Details