Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, and to the best of our knowledge, no existing EA method considers aligning multiple (more than two) KGs. To fill this research gap, in this work, we study a novel problem of aligning multiple KGs and propose an effective framework named MultiEA to solve the problem. First, we embed the entities of all the candidate KGs into a common feature space by a shared KG encoder. Then, we explore three alignment strategies to minimize the distances among pre-aligned entities. In particular, we propose an innovative inference enhancement technique to improve the alignment performance by incorporating high-order similarities. Finally, to verify the effectiveness of MultiEA, we construct two new real-world benchmark datasets and conduct extensive experiments on them. The results show that our MultiEA can effectively and efficiently align multiple KGs in a single pass. We release the source codes of MultiEA at: https://github.com/kepsail/MultiEA.

Affiliate marketing, a component of modern digital marketing, leverages partnerships among merchants, promoters, and consumers to enhance item visibility and drive sales. Promoters act as critical intermediaries, sharing items with their communities to promote items while earning commissions. Accurate prediction of the sharing rate of promoters enables platforms to optimize recommendation performance, thereby improving promotional efficiency. However, existing related methods are mainly designed for consumer-oriented scenarios (C-end), and face significant limitations in modeling the promoters (B-end), which are typically characterized by audience group attachment. Specifically, three core challenges emerge: (1) how to organically integrate audience preferences while maintaining promoter dominance, (2) how to accommodate promoters' diverse interest scopes, and (3) how to capture the complex one-to-many relationships between promoters and their audiences. For Challenge (1), we employ a dynamic routing mechanism based on interest capsules to model the diverse interests of promoters, where audience groups are used to optimize the interest routing via a novel dual-channel attention mechanism, thus allowing audience groups to explicitly participate in the promoter decision-making process with an auxiliary role. For Challenge (2), a parameter-free, confidence-aware interest activation mechanism is introduced to adaptively select sparse interest capsules. For Challenge (3), we pioneer the use of hypergraphs in CTR prediction to model one-to-many relationships between promoters and audiences. Extensive experiments are conducted on two real-world datasets to validate the effectiveness of our approach. Furthermore, the model is deployed on the Alimama platform, which hosts over 100,000 promoters. Online A/B testing results demonstrate that our method achieves a 5.31% average improvement over online baselines.

In recent years, affiliate marketing has emerged as a revenue-sharing strategy where merchants collaborate with promoters to promote their products. It not only increases product exposure but also allows promoters to earn a commission. This paper addresses the pivotal yet under-explored challenge in affiliate marketing: accurately assessing and predicting the contributions of promoters in product promotion. We design a novel metric for evaluating the indirect contributions of the promoter, called propagation scale. Unfortunately, existing time series forecasting techniques fail to deliver accurate predictions due to the propagation scale being influenced by multiple factors and the inherent complexities arising from dynamic scenarios. To address this issue, we decouple the network structure from the node signals and propose a two-stage solution: initially, the basic self-sales and network structure prediction are conducted separately, followed by the synthesis of the propagation scale. Specifically, we design a graph convolution encoding scheme based on descendant neighbors and incorporate hypergraph convolution to efficiently capture complex promotional dynamics. Additionally, three auxiliary tasks are employed: self-sales prediction for base estimations, descendant prediction to synthesize propagation scale, and promoter activation prediction to mitigate high volatility issues. Extensive offline experiments on large-scale industrial datasets validate the superiority of our method. We further deploy our model on Alimama platform with over 100,000 promoters, achieving a 9.29% improvement in GMV and a 5.89% increase in sales volume.

Knowledge graph (KG) alignment aims to integrate different KGs through the linkage of equivalent entities across them, enabling more comprehensive knowledge and facilitating information fusion. Existing methods, whether translation-based or GNN-based, typically solve this problem by projecting entities and relations into a low-dimensional embedding space, each demonstrating unique advantages in aligning a pair of KGs. However, few studies consider combining these approaches to model translation semantics of various orders. To fill this gap, we propose KG2HIN, a novel KG encoder, which innovatively views head entities, relations, and tail entities as three types of nodes, thereby transforming KGs into HINs (heterogeneous information networks). KG2HIN can adaptively learn the importance of various orders of translation semantics by seamlessly combining the HGNN aggregator operator with the translation operator in KG embedding methods. Building upon the KG2HIN encoder, we further develop a network to effectively and efficiently align multiple (more than two) KGs concurrently, a much more challenging task than the traditional pair-KG alignment task. Compared with the state-of-the-art baseline, KG2HIN significantly improves the M-Hits@1 (accuracy) score from 10.25% to 73.05% on the DBP4 dataset and from 41.19% to 97.81% on the DWY-3 dataset, while requiring significantly fewer model parameters and less training time.

Heterogeneous hypergraph is a kind of structural data that contains multiple types of nodes and multiple types of hyperedges. Each hyperedge type corresponds to a specific multi-ary relation (called hyper-relation) among subsets of nodes, which goes beyond traditional pair-wise relations in simple graphs. Existing representation learning methods for heterogeneous hypergraphs typically learn embeddings for nodes and hyperedges based on graph neural networks. Although achieving promising performance, they are still limited in capturing more complex structural features and richer semantics conveyed by the composition of various hyper-relations. To fill this research gap, in this work, we propose the concept of hyper-meta-path for heterogeneous hypergraphs, which is defined as the composition of a sequence of hyper-relations. Besides, we design an attention-based heterogeneous hypergraph neural network (HHNN) to automatically learn the importance of hyper-meta-paths. By exploiting useful ones, HHNN is able to capture more complex structural features to boost the model's performance, as well as leverage their conveyed semantics to improve the model's interpretability. Extensive experiments show that HHNN can achieve significantly better performance than state-of-the-art baselines, and the discovered hyper-meta-paths bring good interpretability for the model predictions. To facilitate the reproducibility of this work, we provide our dataset as well as anonymized source code at: https://github.com/zhengziyu77/HHNN.

Entity Alignment (EA) is to link potential equivalent entities across different knowledge graphs (KGs). Most existing EA methods are supervised as they require the supervision of seed alignments, i.e., manually specified aligned entity pairs. Very recently, several EA studies have made some attempts to get rid of seed alignments. Despite achieving preliminary progress, they still suffer two limitations: (1) The entity embeddings produced by their GNN-like encoders lack personalization since some of the aggregation subpaths are shared between different entities. (2) They cannot fully alleviate the distribution distortion issue between candidate KGs due to the absence of supervised signals. In this work, we propose a novel unsupervised entity alignment approach called UNEA to address the above two issues. First, we parametrically sample a tree neighborhood rooted at each entity, and accordingly develop a tree attention aggregation mechanism to extract a personalized embedding for each entity. Second, we introduce an auxiliary task of maximizing the mutual information between the input and the output of the KG encoder, which serves as a regularization to prevent the distribution distortion. Extensive experiments show that our UNEA achieves a new state-of-the-art for the unsupervised EA task, and can even outperform many existing supervised EA baselines.

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.