Jianbin Huang

Yaming Yang, Ziyu Guan, Zhe Wang et al.

Recent self-supervised pre-training methods on Heterogeneous Information Networks (HINs) have shown promising competitiveness over traditional semi-supervised Heterogeneous Graph Neural Networks (HGNNs). Unfortunately, their performance heavily depends on careful customization of various strategies for generating high-quality positive examples and negative examples, which notably limits their flexibility and generalization ability. In this work, we present SHGP, a novel Self-supervised Heterogeneous Graph Pre-training approach, which does not need to generate any positive examples or negative examples. It consists of two modules that share the same attention-aggregation scheme. In each iteration, the Att-LPA module produces pseudo-labels through structural clustering, which serve as the self-supervision signals to guide the Att-HGNN module to learn object embeddings and attention coefficients. The two modules can effectively utilize and enhance each other, promoting the model to learn discriminative embeddings. Extensive experiments on four real-world datasets demonstrate the superior effectiveness of SHGP against state-of-the-art unsupervised baselines and even semi-supervised baselines. We release our source code at: https://github.com/kepsail/SHGP.

Ruijie Qi, Jianbin Huang, He Li et al.

Traffic signal control is a significant part of the construction of intelligent transportation. An efficient traffic signal control strategy can reduce traffic congestion, improve urban road traffic efficiency and facilitate people's lives. Existing reinforcement learning approaches for traffic signal control mainly focus on learning through a separate neural network. Such an independent neural network may fall into the local optimum of the training results. Worse more, the collected data can only be sampled once, so the data utilization rate is low. Therefore, we propose the Random Ensemble Double DQN Light (RELight) model. It can dynamically learn traffic signal control strategies through reinforcement learning and combine random ensemble learning to avoid falling into the local optimum to reach the optimal strategy. Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of data reuses to improve the problem of low data utilization. In addition, we have conducted sufficient experiments on synthetic data and real-world data to prove that our proposed method can achieve better traffic signal control effects than the existing optimal methods.

Ling-Hao Chen, He Li, Wanyuan Zhang et al.

Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only consider a single kind of interaction, so they cannot deal with various kinds of interactions on multi-view attributed networks. It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks. In this paper, we propose a graph convolution-based framework, named AnomMAN, to detect Anomaly on Multi-view Attributed Networks. To jointly consider attributes and all kinds of interactions on multi-view attributed networks, we use the attention mechanism to define the importance of all views in networks. Since the low-pass characteristic of graph convolution operation filters out most high-frequency signals (aonmaly signals), it cannot be directly applied to anomaly detection tasks. AnomMAN introduces the graph auto-encoder module to turn the disadvantage of low-pass features into an advantage. According to experiments on real-world datasets, AnomMAN outperforms the state-of-the-art models and two variants of our proposed model.

Zhenghao Zhang, Jianbin Huang, Qinglin Tan

Most existing knowledge graphs suffer from incompleteness. Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion. However, in most existing embedding methods, only fact triplets are utilized, and logical rules have not been thoroughly studied for the knowledge base completion task. To overcome the problem, we propose an association rules enhanced knowledge graph attention network (AR-KGAT). The AR-KGAT captures both entity and relation features for high-order neighborhoods of any given entity in an end-to-end manner under the graph attention network framework. The major component of AR-KGAT is an encoder of an effective neighborhood aggregator, which addresses the problems by aggregating neighbors with both association-rules-based and graph-based attention weights. Additionally, the proposed model also encapsulates the representations from multi-hop neighbors of nodes to refine their embeddings. The decoder enables AR-KGAT to be translational between entities and relations while keeping the superior link prediction performance. A logic-like inference pattern is utilized as constraints for knowledge graph embedding. Then, the global loss is minimized over both atomic and complex formulas to achieve the embedding task. In this manner, we learn embeddings compatible with triplets and rules, which are certainly more predictive for knowledge acquisition and inference. We conduct extensive experiments on two benchmark datasets: WN18RR and FB15k-237, for two knowledge graph completion tasks: the link prediction and triplet classification to evaluate the proposed AR-KGAT model. The results show that the proposed AR-KGAT model achieves significant and consistent improvements over state-of-the-art methods.

Zhenghao Zhang, Jianbin Huang, Qinglin Tan

Most existing Heterogeneous Information Network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of realworld networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environment. To tackle above challenges, we propose a novel framework for incorporating temporal information into HIN embedding, denoted as Multi-View Dynamic HIN Embedding (MDHNE), which can efficiently preserve evolution patterns of implicit relationships from different views in updating node representations over time. We first transform HIN to a series of homogeneous networks corresponding to different views. Then our proposed MDHNE applies Recurrent Neural Network (RNN) to incorporate evolving pattern of complex network structure and semantic relationships between nodes into latent embedding spaces, and thus the node representations from multiple views can be learned and updated when HIN evolves over time. Moreover, we come up with an attention based fusion mechanism, which can automatically infer weights of latent representations corresponding to different views by minimizing the objective function specific for different mining tasks. Extensive experiments clearly demonstrate that our MDHNE model outperforms state-of-the-art baselines on three real-world dynamic datasets for different network mining tasks.