3,474 research outputs found
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
In this work, we study semi-supervised multi-label node classification
problem in attributed graphs. Classic solutions to multi-label node
classification follow two steps, first learn node embedding and then build a
node classifier on the learned embedding. To improve the discriminating power
of the node embedding, we propose a novel collaborative graph walk, named
Multi-Label-Graph-Walk, to finely tune node representations with the available
label assignments in attributed graphs via reinforcement learning. The proposed
method formulates the multi-label node classification task as simultaneous
graph walks conducted by multiple label-specific agents. Furthermore, policies
of the label-wise graph walks are learned in a cooperative way to capture first
the predictive relation between node labels and structural attributes of
graphs; and second, the correlation among the multiple label-specific
classification tasks. A comprehensive experimental study demonstrates that the
proposed method can achieve significantly better multi-label classification
performance than the state-of-the-art approaches and conduct more efficient
graph exploration.Comment: Accepted for IEEE International Conference on Data Mining (ICDM) 201
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Predicting properties of nodes in a graph is an important problem with
applications in a variety of domains. Graph-based Semi-Supervised Learning
(SSL) methods aim to address this problem by labeling a small subset of the
nodes as seeds and then utilizing the graph structure to predict label scores
for the rest of the nodes in the graph. Recently, Graph Convolutional Networks
(GCNs) have achieved impressive performance on the graph-based SSL task. In
addition to label scores, it is also desirable to have confidence scores
associated with them. Unfortunately, confidence estimation in the context of
GCN has not been previously explored. We fill this important gap in this paper
and propose ConfGCN, which estimates labels scores along with their confidences
jointly in GCN-based setting. ConfGCN uses these estimated confidences to
determine the influence of one node on another during neighborhood aggregation,
thereby acquiring anisotropic capabilities. Through extensive analysis and
experiments on standard benchmarks, we find that ConfGCN is able to outperform
state-of-the-art baselines. We have made ConfGCN's source code available to
encourage reproducible research.Comment: Accepted at AISTATS 201
Link Prediction in Social Networks: the State-of-the-Art
In social networks, link prediction predicts missing links in current
networks and new or dissolution links in future networks, is important for
mining and analyzing the evolution of social networks. In the past decade, many
works have been done about the link prediction in social networks. The goal of
this paper is to comprehensively review, analyze and discuss the
state-of-the-art of the link prediction in social networks. A systematical
category for link prediction techniques and problems is presented. Then link
prediction techniques and problems are analyzed and discussed. Typical
applications of link prediction are also addressed. Achievements and roadmaps
of some active research groups are introduced. Finally, some future challenges
of the link prediction in social networks are discussed.Comment: 38 pages, 13 figures, Science China: Information Science, 201
Enhanced Network Embeddings via Exploiting Edge Labels
Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. While achieving competitive performance
on a variety of network inference tasks such as node classification and link
prediction, these methods treat the relations between nodes as a binary
variable and ignore the rich semantics of edges. In this work, we attempt to
learn network embeddings which simultaneously preserve network structure and
relations between nodes. Experiments on several real-world networks illustrate
that by considering different relations between different node pairs, our
method is capable of producing node embeddings of higher quality than a number
of state-of-the-art network embedding methods, as evaluated on a challenging
multi-label node classification task.Comment: CIKM 201
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
In many recommender systems, users and items are associated with attributes,
and users show preferences to items. The attribute information describes
users'(items') characteristics and has a wide range of applications, such as
user profiling, item annotation, and feature-enhanced recommendation. As
annotating user (item) attributes is a labor intensive task, the attribute
values are often incomplete with many missing attribute values. Therefore, item
recommendation and attribute inference have become two main tasks in these
platforms. Researchers have long converged that user (item) attributes and the
preference behavior are highly correlated. Some researchers proposed to
leverage one kind of data for the remaining task, and showed to improve
performance. Nevertheless, these models either neglected the incompleteness of
user (item) attributes or regarded the correlation of the two tasks with simple
models, leading to suboptimal performance of these two tasks. To this end, in
this paper, we define these two tasks in an attributed user-item bipartite
graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for
joint item recommendation and attribute inference. The key idea of AGCN is to
iteratively perform two parts: 1) Learning graph embedding parameters with
previously learned approximated attribute values to facilitate two tasks; 2)
Sending the approximated updated attribute values back to the attributed graph
for better graph embedding learning. Therefore, AGCN could adaptively adjust
the graph embedding learning parameters by incorporating both the given
attributes and the estimated attribute values, in order to provide weakly
supervised information to refine the two tasks. Extensive experimental results
on three real-world datasets clearly show the effectiveness of the proposed
model.Comment: Accepted by SIGIR202
GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models
Modern data analysis pipelines are becoming increasingly complex due to the
presence of multi-view information sources. While graphs are effective in
modeling complex relationships, in many scenarios a single graph is rarely
sufficient to succinctly represent all interactions, and hence multi-layered
graphs have become popular. Though this leads to richer representations,
extending solutions from the single-graph case is not straightforward.
Consequently, there is a strong need for novel solutions to solve classical
problems, such as node classification, in the multi-layered case. In this
paper, we consider the problem of semi-supervised learning with multi-layered
graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for
community discovery, we argue that feature learning with random node
attributes, using graph neural networks, can be more effective. To this end, we
propose to use attention models for effective feature learning, and develop two
novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer
dependencies for building multi-layered graph embeddings. Using empirical
studies on several benchmark datasets, we evaluate the proposed approaches and
demonstrate significant performance improvements in comparison to
state-of-the-art network embedding strategies. The results also show that using
simple random features is an effective choice, even in cases where explicit
node attributes are not available
COSINE: Compressive Network Embedding on Large-scale Information Networks
There is recently a surge in approaches that learn low-dimensional embeddings
of nodes in networks. As there are many large-scale real-world networks, it's
inefficient for existing approaches to store amounts of parameters in memory
and update them edge after edge. With the knowledge that nodes having similar
neighborhood will be close to each other in embedding space, we propose COSINE
(COmpresSIve NE) algorithm which reduces the memory footprint and accelerates
the training process by parameters sharing among similar nodes. COSINE applies
graph partitioning algorithms to networks and builds parameter sharing
dependency of nodes based on the result of partitioning. With parameters
sharing among similar nodes, COSINE injects prior knowledge about higher
structural information into training process which makes network embedding more
efficient and effective. COSINE can be applied to any embedding lookup method
and learn high-quality embeddings with limited memory and shorter training
time. We conduct experiments of multi-label classification and link prediction,
where baselines and our model have the same memory usage. Experimental results
show that COSINE gives baselines up to 23% increase on classification and up to
25% increase on link prediction. Moreover, time of all representation learning
methods using COSINE decreases from 30% to 70%
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations for
graph-structured data. Graph representation learning methods have generally
fallen into three main categories, based on the availability of labeled data.
The first, network embedding (such as shallow graph embedding or graph
auto-encoders), focuses on learning unsupervised representations of relational
structure. The second, graph regularized neural networks, leverages graphs to
augment neural network losses with a regularization objective for
semi-supervised learning. The third, graph neural networks, aims to learn
differentiable functions over discrete topologies with arbitrary structure.
However, despite the popularity of these areas there has been surprisingly
little work on unifying the three paradigms. Here, we aim to bridge the gap
between graph neural networks, network embedding and graph regularization
models. We propose a comprehensive taxonomy of representation learning methods
for graph-structured data, aiming to unify several disparate bodies of work.
Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which
generalizes popular algorithms for semi-supervised learning on graphs (e.g.
GraphSage, Graph Convolutional Networks, Graph Attention Networks), and
unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc)
into a single consistent approach. To illustrate the generality of this
approach, we fit over thirty existing methods into this framework. We believe
that this unifying view both provides a solid foundation for understanding the
intuition behind these methods, and enables future research in the area
A Survey of Heterogeneous Information Network Analysis
Most real systems consist of a large number of interacting, multi-typed
components, while most contemporary researches model them as homogeneous
networks, without distinguishing different types of objects and links in the
networks. Recently, more and more researchers begin to consider these
interconnected, multi-typed data as heterogeneous information networks, and
develop structural analysis approaches by leveraging the rich semantic meaning
of structural types of objects and links in the networks. Compared to widely
studied homogeneous network, the heterogeneous information network contains
richer structure and semantic information, which provides plenty of
opportunities as well as a lot of challenges for data mining. In this paper, we
provide a survey of heterogeneous information network analysis. We will
introduce basic concepts of heterogeneous information network analysis, examine
its developments on different data mining tasks, discuss some advanced topics,
and point out some future research directions.Comment: 45 pages, 12 figure
Directed Graph Convolutional Network
Graph Convolutional Networks (GCNs) have been widely used due to their
outstanding performance in processing graph-structured data. However, the
undirected graphs limit their application scope. In this paper, we extend
spectral-based graph convolution to directed graphs by using first- and
second-order proximity, which can not only retain the connection properties of
the directed graph, but also expand the receptive field of the convolution
operation. A new GCN model, called DGCN, is then designed to learn
representations on the directed graph, leveraging both the first- and
second-order proximity information. We empirically show the fact that GCNs
working only with DGCNs can encode more useful information from graph and help
achieve better performance when generalized to other models. Moreover,
extensive experiments on citation networks and co-purchase datasets demonstrate
the superiority of our model against the state-of-the-art methods
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