22 research outputs found
An Effective and Efficient Graph Representation Learning Approach for Big Graphs
In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be naturally represented by
multi-view networks where the different views encode different types of
relationships (e.g., friendship, shared interests in music, etc.) between
real-world individuals or entities. There is an urgent need for methods to
obtain low-dimensional, information preserving and typically nonlinear
embeddings of such multi-view networks. However, most of the work on multi-view
learning focuses on data that lack a network structure, and most of the work on
network embeddings has focused primarily on single-view networks. Against this
background, we consider the multi-view network representation learning problem,
i.e., the problem of constructing low-dimensional information preserving
embeddings of multi-view networks. Specifically, we investigate a novel
Generative Adversarial Network (GAN) framework for Multi-View Network
Embedding, namely MEGAN, aimed at preserving the information from the
individual network views, while accounting for connectivity across (and hence
complementarity of and correlations between) different views. The results of
our experiments on two real-world multi-view data sets show that the embeddings
obtained using MEGAN outperform the state-of-the-art methods on node
classification, link prediction and visualization tasks.Comment: Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, IJCAI-1
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
With the great success of graph embedding model on both academic and industry
area, the robustness of graph embedding against adversarial attack inevitably
becomes a central problem in graph learning domain. Regardless of the fruitful
progress, most of the current works perform the attack in a white-box fashion:
they need to access the model predictions and labels to construct their
adversarial loss. However, the inaccessibility of model predictions in real
systems makes the white-box attack impractical to real graph learning system.
This paper promotes current frameworks in a more general and flexible sense --
we demand to attack various kinds of graph embedding model with black-box
driven. To this end, we begin by investigating the theoretical connections
between graph signal processing and graph embedding models in a principled way
and formulate the graph embedding model as a general graph signal process with
corresponding graph filter. As such, a generalized adversarial attacker:
GF-Attack is constructed by the graph filter and feature matrix. Instead of
accessing any knowledge of the target classifiers used in graph embedding,
GF-Attack performs the attack only on the graph filter in a black-box attack
fashion. To validate the generalization of GF-Attack, we construct the attacker
on four popular graph embedding models. Extensive experimental results validate
the effectiveness of our attacker on several benchmark datasets. Particularly
by using our attack, even small graph perturbations like one-edge flip is able
to consistently make a strong attack in performance to different graph
embedding models.Comment: Accepted by the AAAI 202
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Graph node embedding aims at learning a vector representation for all nodes
given a graph. It is a central problem in many machine learning tasks (e.g.,
node classification, recommendation, community detection). The key problem in
graph node embedding lies in how to define the dependence to neighbors.
Existing approaches specify (either explicitly or implicitly) certain
dependencies on neighbors, which may lead to loss of subtle but important
structural information within the graph and other dependencies among neighbors.
This intrigues us to ask the question: can we design a model to give the
maximal flexibility of dependencies to each node's neighborhood. In this paper,
we propose a novel graph node embedding (named PINE) via a novel notion of
partial permutation invariant set function, to capture any possible dependence.
Our method 1) can learn an arbitrary form of the representation function from
the neighborhood, withour losing any potential dependence structures, and 2) is
applicable to both homogeneous and heterogeneous graph embedding, the latter of
which is challenged by the diversity of node types. Furthermore, we provide
theoretical guarantee for the representation capability of our method for
general homogeneous and heterogeneous graphs. Empirical evaluation results on
benchmark data sets show that our proposed PINE method outperforms the
state-of-the-art approaches on producing node vectors for various learning
tasks of both homogeneous and heterogeneous graphs.Comment: 24 pages, 4 figures, 3 tables. arXiv admin note: text overlap with
arXiv:1805.1118