886 research outputs found
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. In the meantime, network embedding has emerged as a convenient
tool to mine and learn from networked data. As a result, it is of interest to
develop HIN embedding methods. However, the heterogeneity in HINs introduces
not only rich information but also potentially incompatible semantics, which
poses special challenges to embedding learning in HINs. With the intention to
preserve the rich yet potentially incompatible information in HIN embedding, we
propose to study the problem of comprehensive transcription of heterogeneous
information networks. The comprehensive transcription of HINs also provides an
easy-to-use approach to unleash the power of HINs, since it requires no
additional supervision, expertise, or feature engineering. To cope with the
challenges in the comprehensive transcription of HINs, we propose the HEER
algorithm, which embeds HINs via edge representations that are further coupled
with properly-learned heterogeneous metrics. To corroborate the efficacy of
HEER, we conducted experiments on two large-scale real-words datasets with an
edge reconstruction task and multiple case studies. Experiment results
demonstrate the effectiveness of the proposed HEER model and the utility of
edge representations and heterogeneous metrics. The code and data are available
at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, London, United Kingdom,
ACM, 201
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Networks found in the real-world are numerous and varied. A common type of
network is the heterogeneous network, where the nodes (and edges) can be of
different types. Accordingly, there have been efforts at learning
representations of these heterogeneous networks in low-dimensional space.
However, most of the existing heterogeneous network embedding methods suffer
from the following two drawbacks: (1) The target space is usually Euclidean.
Conversely, many recent works have shown that complex networks may have
hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually
rely on meta-paths, which require domain-specific prior knowledge for meta-path
selection. Additionally, different down-streaming tasks on the same network
might require different meta-paths in order to generate task-specific
embeddings. In this paper, we propose a novel self-guided random walk method
that does not require meta-path for embedding heterogeneous networks into
hyperbolic space. We conduct thorough experiments for the tasks of network
reconstruction and link prediction on two public datasets, showing that our
model outperforms a variety of well-known baselines across all tasks.Comment: In proceedings of the 35th AAAI Conference on Artificial Intelligenc
ASIAM-HGNN: Automatic Selection and Interpretable Aggregation of Meta-Path Instances for Heterogeneous Graph Neural Network
In heterogeneous information network (HIN)-based applications, the existing methods usually use Heterogeneous Graph Neural Networks (HGNN) to handle some complex tasks. However, these methods still have some shortcomings: 1) they manually pre-select some meta-paths and thus some important ones are missing, while the missing ones still contains the information and features of the node in the entire graph structure; and 2) they have no high interpretability since they do not consider the logical sequences in an HIN. In order to deal with them, we propose ASIAM-HGNN: a heterogeneous graph neural network combined with the automatic selection and interpretable aggregation of meta-path instances. Our model can automatically filter important meta paths for each node, while preserving the logical sequence between nodes, so as to solve the problems existing in other models. A group of experiments are conducted on real-world datasets, and the results demonstrate that the models learned by our method have a better performance in most of task scenarios
Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks
Heterogeneous graph neural networks (HGNNs) have exhibited exceptional
efficacy in modeling the complex heterogeneity in heterogeneous information
networks (HINs). The critical advantage of HGNNs is their ability to handle
diverse node and edge types in HINs by extracting and utilizing the abundant
semantic information for effective representation learning. However, as a
widespread phenomenon in many real-world scenarios, the class-imbalance
distribution in HINs creates a performance bottleneck for existing HGNNs. Apart
from the quantity imbalance of nodes, another more crucial and distinctive
challenge in HINs is semantic imbalance. Minority classes in HINs often lack
diverse and sufficient neighbor nodes, resulting in biased and incomplete
semantic information. This semantic imbalance further compounds the difficulty
of accurately classifying minority nodes, leading to the performance
degradation of HGNNs. To tackle the imbalance of minority classes and
supplement their inadequate semantics, we present the first method for the
semantic imbalance problem in imbalanced HINs named Semantic-aware Node
Synthesis (SNS). By assessing the influence on minority classes, SNS adaptively
selects the heterogeneous neighbor nodes and augments the network with
synthetic nodes while preserving the minority semantics. In addition, we
introduce two regularization approaches for HGNNs that constrain the
representation of synthetic nodes from both semantic and class perspectives to
effectively suppress the potential noises from synthetic nodes, facilitating
more expressive embeddings for classification. The comprehensive experimental
study demonstrates that SNS consistently outperforms existing methods by a
large margin in different benchmark datasets
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