Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of expressive power from GNN via learning$individual$node embeddings based on their structural neighbors, largely ignoring the potential correlations between nodes and leading to sub-optimal performance.How to establish correlations among multiple node embeddings and improve the expressive power of HGNN is still an open problem.To solve the above problem, we propose a simple and effective technique called heterogeneous distance encoding (HDE) to fundamentally improve the expressive power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we can learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed HDE relies only on the graph structure and ensures the inductive ability of HGNN. We also propose an efficient HDE algorithm that can significantly reduce the computational overhead. Significant improvements on both transductive and inductive tasks over four real-world graphs demonstrate the effectiveness of HDE in improving the expressive power of HGNN.

Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Recently, some works attempt to generalize GNN to heterogeneous graph which contains different types of nodes and links. Heterogeneous graph neural networks (HeteGNNs) usually follow two steps: aggregate neighbors via single meta-path and then aggregate rich semantics via multiple meta-paths. However, we discover an important semantic confusion phenomenon in HeteGNNs, i.e., with the growth of model depth, the learned node embeddings become indistinguishable, leading to the performance degradation of HeteGNNs. We explain semantic confusion by theoretically deriving that HeteGNNs and multiple meta-paths based random walk are essentially equivalent. Following the theoretical analysis, we propose a novel Heterogeneous graph Propagation Network (HPN) to alleviate the semantic confusion. Specically, the semantic propagation mechanism of HPN absorbes nodes local semantic with a proper weight during aggregating process, which makes HPN capture the characteristics of each node and learn distinguishable node embedding with deeper HeteGNN architecture. Then, the semantic fusion mechanism is designed to learn the importance of meta-path and fuse them judiciously. Extensive experimental results on three datasets show the superior performance of the proposed HPN over the state-of-the-arts.

In E-commerce recommendation, Click-Through Rate (CTR) prediction has been extensively studied in both academia and industry to enhance user experience and platform benefits. At present, most popular CTR prediction methods are concatenation-based models that represent items by simply merging multiple heterogeneous features including ID, visual, and text features into a large vector. As these heterogeneous modalities have moderately different properties, directly concatenating them without mining the correlation and reducing the redundancy are unlikely to achieve the optimal fusion results. Besides, these concatenation-based models treat all modalities equally for each user and overlook the fact that users tend to pay unequal attention to information of various modalities when browsing items in the real scenario. To address the above issues, this paper proposes a generative multimodal fusion framework (GMMF) for CTR prediction task. To eliminate the redundancy and strength the complementary of multimodal features, GMMF generates the new visual and text representations by a Difference-Set network (DSN). These representations are non-overlapping with the information conveyed by ID embedding. Specifically, DSN maps ID embedding into visual and text modalities and depicts the difference between multiple modalities based on their properties. Besides, GMMF learns unequal weights to multiple modalities with a Modal-Interest network (MIN) modeling users' preference on heterogeneous modalities. These weights reflect the usual habits and hobbies of users. Finally, We conduct extensive experiments on both public and collected industrial datasets, and the results show that GMMF greatly improves performance and achieves state-of-the-art performance.

The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Significantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a specific item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user influence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Specifically, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Offline experiments demonstrate the superiority of the proposed HGSRec with significant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.

Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of representational power from GNN via learning individual node embeddings based on their neighbors, largely ignoring the potential correlations between nodes. In fact, the complex correlation between nodes (e.g., distance) is crucial for many graph mining tasks. How to establish correlations between multiple node embeddings and improve the representational power of HGNN is still an open problem. To solve it, we propose a heterogeneous distance encoding (HDE) technique to fundamentally improve the representational power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we propose a novel distance encoding based heterogeneous graph neural network (called DHN), which is able to learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed DHN relies only on the graph structure and ensures the inductive ability of HGNN. Significant improvements over four real-world graphs demonstrate the representational power of HDE.

Promotion recommendation, as a new recommendation paradigm in recent years, plays an important role in stimulating the purchase desire of users and maximizing the total revenue. Different from previous recommendations (e.g., item/group recommendation), promotion recommendation aims to select a set of K items based on all user preferences in selection phase and maximize the total revenue in delivery phase. Although these two phases are closely related with each other, existing methods usually focus on item selection in selection phase, largely ignoring the delivery phase and leading to sub-optimal performance. To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. When selecting K items, the comb-K recommendation is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences. Specifically, we propose a novel heterogeneous graph convolutional network to estimate user preference and propose the user-level comb-K recommendation model through solving a binary combination optimization problem. In order to handle combination explosion for large-scale users, we furtherly cluster massive users into limited groups and present a group-level comb-K recommendation model in which a novel heterogeneous graph pooling network is proposed to perform user clustering and estimate group preference. In addition, considering the ”long tail” phenomenon in e-commerce, we design a restricted neighbor heuristic search to accelerate the solving process. Extensive experiments on four datasets demonstrate the superiority of comb-K model for large-scale promotion recommendation. On billion-scale data, when clustering 2.5 × 107 users into 103 groups, our model is able to preserve 98.7% personalized preferences in group-level and significantly improves the Total Click and Hit Ratio by 9.35% and 7.14%, respectively.

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although most of GNNs basically follow a message passing manner, litter effort has been made to discover and analyze their essential relations. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, not only provides a macroscopic view on surveying the relations between different GNNs, but also further opens up new opportunities for flexibly designing new GNNs. With the proposed framework, we discover that existing works usually utilize naïve graph convolutional kernels for feature fitting function, and we further develop two novel objective functions considering adjustable graph kernels showing low-pass or high-pass filtering capabilities respectively. Moreover, we provide the convergence proofs and expressive power comparisons for the proposed models. Extensive experiments on benchmark datasets clearly show that the proposed GNNs not only outperform the state-of-the-art methods but also have good ability to alleviate over-smoothing, and further verify the feasibility for designing GNNs with our unified optimization framework.

Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.

Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, Xiaoli Li. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.