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MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
[article]
2022
arXiv
pre-print
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MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings. ...
These embeddings can then be used for several tasks such as recommendation and search. ...
Both DistDGL and Aligraph rely on multi-processing for "on-thefly" sampling of node's neighbors. ...
arXiv:2205.10666v1
fatcat:3pf3hwzyc5hubfqjqifmefh2zy
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2108.07574v1 fatcat:e4sss7foj5cz7jxxuo4avnmqiq
To close the gap between collaborative filtering and product search, we propose a Hierarchical Heterogeneous Graph Neural Network (HHGNN) approach in this paper. ...
The sequence edge accounts for the syntax formulation from word nodes to sentence nodes; the composition edge aggregates the semantic features to the user and product nodes; and the interaction edge on ...
The spectral-based methods define the convolution operation on the basis of the graph signal processing theory [26] . For instance, Bruna et al. ...
arXiv:2108.07574v2
fatcat:k2fxc3bonvaxlmo2em46w5es3m
Efficient and Effective Multi-Modal Queries through Heterogeneous Network Embedding
2021
IEEE Transactions on Knowledge and Data Engineering
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CONCLUSIONS In this paper, we presented a new direction for multimodal IR that relies on an embedding of a heterogeneous information network. ...
Based thereon, we proposed a novel network embedding model to obtain a vectorized representation of a HIN and a technique to construct a query embedding based on the HIN embedding through mapping and combination ...
Two nodes are considered to be close, if one occurs on the random walk from the other one. Network embedding based on random walks can be considered as an application of word embeddings for graphs. ...
doi:10.1109/tkde.2021.3052871
fatcat:eff3dykxhbhgzeywvpfkjswcju
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1905.07374v1 fatcat:twlhdgpa4vgovpnjq3xqx6psfi
We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. ...
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. ...
Acknowledgements We would like to thank Johannes Welbl for running evaluation on our submitted model. ...
arXiv:1905.07374v2
fatcat:epznpgcp7vgovminxdcn53t5yq
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
2021
IEEE Transactions on Knowledge and Data Engineering
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Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. ...
However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. ...
In this paper, we focus on semisupervised node classification, which is one of the most popular applications for heterogeneous graph embedding models [5] - [7] . ...
doi:10.1109/tkde.2021.3100529
fatcat:azur4cdwafg3zhk27tnssqysza
Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
[article]
2021
arXiv
pre-print
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The name "crname" is a metaphor for the pattern of the model: regard the subjects of queries as the start points of clues, take the reasoning entities as bridge points, and consider the latent candidate ...
Spectral models based on graph convolutional networks grant the inferring abilities and lead to competitive results, however, part of them still face the challenge of analyzing the reasoning in a human-understandable ...
ACKNOWLEDGMENT The authors would like to thank the UCL machine reading group who creates the QANGAROO dataset and their help for evaluating our model. ...
arXiv:2107.00841v1
fatcat:d72l6nda5vgpbn2pz7djddfv4u
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
[article]
2021
arXiv
pre-print
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Dynamic Snapshot (DDS) linkage design for graph construction and a Lambda Neural Networks (LNN) architecture for effective inference with Graph Neural Networks embeddings. ...
In order to leverage graph networks to decrease fraud rate efficiently and guarantee the information flow passed through neighbors only from the past of the checkouts, we first present a novel Directed ...
Conclusions We present an approach LNN on DDS graph, to leverage both graph structure and time snapshot features for fraud transaction detection. ...
arXiv:2110.04559v1
fatcat:pcvlklnhnnesvek4rnbanekxhq
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
[article]
2021
arXiv
pre-print
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Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. ...
However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. ...
In this paper, we focus on semisupervised node classification, which is one of the most popular applications for heterogeneous graph embedding models [5] - [7] . ...
arXiv:2104.01711v2
fatcat:uozthcnesjbszdf2ciauvvgrai
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
[article]
2022
arXiv
pre-print
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Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path ...
Defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. ...
For instance, ESim [11] learns node embeddings under the guidance of meta-paths and uses the embeddings for relevance measure in heterogeneous graphs. ...
arXiv:2208.06144v1
fatcat:whp7xd6w2rgfvhpoe4n53prdju
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. ...
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. ...
Acknowledgements We would like to thank Johannes Welbl from University College London for running evaluation on our submitted model. ...
doi:10.18653/v1/p19-1260
dblp:conf/acl/TuWHTHZ19
fatcat:os2vkhkzynh5bl2xk44yzhdpkq
BRIGHT – Graph Neural Networks in Real-Time Fraud Detection
[article]
2022
arXiv
pre-print
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Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. ...
For the inference stage, our speedup is on average 7.8× compared to the traditional GNN. ...
We present a novel BRIGHT framework for efficient end-to-end learning and real-time inference without the leakage of future information. ...
arXiv:2205.13084v1
fatcat:vwgov5r255d2no6hvnm4zirdn4
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
[article]
2020
arXiv
pre-print
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Experiments on real-world dataset show that our method outperforms state-of-the-art RL methods and discovers more novel paths during the training procedure. ...
Our solution uses graph neural network (GNN) for encoding the neighborhood information and utilizes entity types to prune the action space. ...
In light of recent work on heterogeneous networks that have demonstrated the importance of heterogeneous information [5, 36, 24, 11] and local neighborhood information [31, 37] in graph mining, we ...
arXiv:2003.06050v1
fatcat:jfw2bdpgrbe7lbxjgtthkg5ggi
Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
[article]
2021
arXiv
pre-print
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PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and clustering. ...
Computing PathSim scores on large graphs is computationally challenging due to its high time and storage complexity. ...
ACKNOWLEDGEMENTS The authors of this paper were supported the NSFC Fund (U20B2053 ) from the NSFC of China, the RIF (R6020-19 and R6021-20) and the GRF (16211520) from RGC of Hong Kong, the MHKJFS (MHP ...
arXiv:2109.01549v1
fatcat:tnhpw3onebdwdlgf4waibtwa44
Context-aware Heterogeneous Graph Attention Network for User Behavior Prediction in Local Consumer Service Platform
[article]
2021
arXiv
pre-print
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Hence, in this paper, we propose a context-aware heterogeneous graph attention network (CHGAT) to dynamically generate the representation of the user and to estimate the probability for future behavior ...
Specifically, we first construct the meta-path based heterogeneous graphs with the historical behaviors from multiple sources and comprehend heterogeneous vertices in the graph with a novel unified knowledge ...
And the detailed aggregation process of our constructed heterogeneous graph is then formulated. ...
arXiv:2106.14652v2
fatcat:wlsiou5cyvbsbdlavhlxmxeodq
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
[article]
2020
arXiv
pre-print
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survey and categorize the state-of-the-art HG embedding methods based on the information they used in the learning process to address the challenges posed by the HG heterogeneity. ...
We first introduce the basic concepts of HG and discuss the unique challenges brought by the heterogeneity for HG embedding in comparison with homogeneous graph representation learning; and then we systemically ...
It constructs a useritem-query heterogeneous graph and designs a meta-pathguided HGNN to learn the embedding of users, items and queries, which can capture the intent of users. ...
arXiv:2011.14867v1
fatcat:phfoxj7qsrfshfednomeok7pau
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