45,218 research outputs found
Learning Heterogeneous Network Embedding From Text and Links
Finding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10 -9 , indicating very significant improvement
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Heterogeneous information network has been widely used to alleviate sparsity
and cold start problems in recommender systems since it can model rich context
information in user-item interactions. Graph neural network is able to encode
this rich context information through propagation on the graph. However,
existing heterogeneous graph neural networks neglect entanglement of the latent
factors stemming from different aspects. Moreover, meta paths in existing
approaches are simplified as connecting paths or side information between node
pairs, overlooking the rich semantic information in the paths. In this paper,
we propose a novel disentangled heterogeneous graph attention network DisenHAN
for top- recommendation, which learns disentangled user/item representations
from different aspects in a heterogeneous information network. In particular,
we use meta relations to decompose high-order connectivity between node pairs
and propose a disentangled embedding propagation layer which can iteratively
identify the major aspect of meta relations. Our model aggregates corresponding
aspect features from each meta relation for the target user/item. With
different layers of embedding propagation, DisenHAN is able to explicitly
capture the collaborative filtering effect semantically. Extensive experiments
on three real-world datasets show that DisenHAN consistently outperforms
state-of-the-art approaches. We further demonstrate the effectiveness and
interpretability of the learned disentangled representations via insightful
case studies and visualization.Comment: Accepted at CIKM202
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many
real-world applications. However, existing methods mainly focus on networks
with single-typed nodes/edges and cannot scale well to handle large networks.
Many real-world networks consist of billions of nodes and edges of multiple
types, and each node is associated with different attributes. In this paper, we
formalize the problem of embedding learning for the Attributed Multiplex
Heterogeneous Network and propose a unified framework to address this problem.
The framework supports both transductive and inductive learning. We also give
the theoretical analysis of the proposed framework, showing its connection with
previous works and proving its better expressiveness. We conduct systematical
evaluations for the proposed framework on four different genres of challenging
datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results
demonstrate that with the learned embeddings from the proposed framework, we
can achieve statistically significant improvements (e.g., 5.99-28.23% lift by
F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link
prediction. The framework has also been successfully deployed on the
recommendation system of a worldwide leading e-commerce company, Alibaba Group.
Results of the offline A/B tests on product recommendation further confirm the
effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn
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