Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influence of hidden confounders, which remain unobserved at the individual unit level. To address this limitation, researchers have utilized graph neural networks to aggregate neighbors' features to capture the hidden confounders and mitigate confounding bias by minimizing the discrepancy of confounder representations between the treated and control groups. Despite the success of these approaches, practical scenarios often treat all features as confounders and involve substantial differences in feature distributions between the treated and control groups. Confusing the adjustment and confounder and enforcing strict balance on the confounder representations could potentially undermine the effectiveness of outcome prediction. To mitigate this issue, we propose a novel framework called the Graph Disentangle Causal model (GDC) to conduct ITE estimation in the network setting. GDC utilizes a causal disentangle module to separate unit features into adjustment and confounder representations. Then we design a graph aggregation module consisting of three distinct graph aggregators to obtain adjustment, confounder, and counterfactual confounder representations. Finally, a causal constraint module is employed to enforce the disentangled representations as true causal factors. The effectiveness of our proposed method is demonstrated by conducting comprehensive experiments on two networked datasets.

Modern recommendation platforms frequently encompass multiple domains to cater to the varied preferences of users. Recently, cross-domain learning has gained traction as a significant paradigm within the context of recommendation systems, enabling the leveraging of rich information from a well-endowed source domain to enhance a target domain, often limited by inadequate data resources. A primary concern in cross-domain recommendation is the mitigation of negative transfer-ensuring the selective transference of pertinent knowledge from the source (domain-shared knowledge) while maintaining the integrity of domain-unique insights within the target domain (domain-specific knowledge).

In the realm of time series analysis, accurately measuring similarity is crucial for applications such as forecasting, anomaly detection, and clustering. However, existing metrics often fail to capture the complex, multidimensional nature of time series data, limiting their effectiveness and application. This paper introduces the Structured Similarity Index Measure for Time Series (TS3IM), a novel approach inspired by the success of the Structural Similarity Index Measure (SSIM) in image analysis, tailored to address these limitations by assessing structural similarity in time series. TS3IM evaluates multiple dimensions of similarity—trend, variability, and structural integrity—offering a more nuanced and comprehensive measure. This metric represents a significant leap forward, providing a robust tool for analyzing temporal data and offering more accurate and comprehensive sequence analysis and decision support in fields such as monitoring power consumption, analyzing traffic flow, and adversarial recognition. Our extensive experimental results also show that compared with traditional methods that rely heavily on computational correlation, TS3IM is 1.87 times more similar to Dynamic Time Warping (DTW) in evaluation results and improves by more than 50% in adversarial recognition.

At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.

Crowd counting plays an important role in event planning, video surveillance, and other fields. At present, the development of single-view crowd counting is relatively mature, but due to the limitation of a single field of view, it is not suitable in some dense occluded scenes. Multi-view crowd counting uses images from multiple views to estimate the number of crowds in the current scene. Most multi-view crowd counting methods based on the deep convolutional neural networks use independent and identical view branches, which bring massive redundant features and increase the model's complexity. This paper proposes a light-weight and branch-shared convolutional neural networks method, which decreases the number of learnable parameters. This method uses the same view branch to extract multi-scale feature maps with images from different views. The camera-view feature maps will be projected into the same plane in world space to fuse, then the scene-level feature maps extracted at different scales are regressed to a scene-level density map. Extensive experiments are conducted on two public datasets (PETS2009, CityStreet), and compared with five existing methods, this method can achieve better performance.

Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit the knowledge graph to capture target-specific knowledge relationships in recommender systems, we need to distill the knowledge graph to reserve the useful information and refine the knowledge to capture the users' preferences. To address the issues, we propose Knowledge-aware Conditional Attention Networks (KCAN), which is an end-to-end model to incorporate knowledge graph into a recommender system. Specifically, we use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then given a target, i.e., a user-item pair, we automatically distill the knowledge graph into the target-specific subgraph based on the knowledge-aware attention. Afterward, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations. Therefore, we can gain both representability and personalization to achieve overall performance. Experimental results on real-world datasets demonstrate the effectiveness of our framework over the state-of-the-art algorithms.

In tag-enhanced video recommendation systems, videos are attached with some tags that highlight the contents of videos from different aspects. Tag ranking in such recommendation systems provides personalized tag lists for videos from their tag candidates. A better tag ranking model could attract users to click more tags, enter their corresponding tag channels, and watch more tag-specific videos, which improves both tag click rate and video watching time. However, most conventional tag ranking models merely concentrate on tag-video relevance or tag-related behaviors, ignoring the rich information in video-related behaviors. We should consider user preferences on both tags and videos. In this paper, we propose a novel Graph neural network based tag ranking (GraphTR) framework on a huge heterogeneous network with video, tag, user and media. We design a novel graph neural network that combines multi-field transformer, GraphSAGE and neural FM layers in node aggregation. We also propose a neighbor-similarity based loss to encode various user preferences into heterogeneous node representations. In experiments, we conduct both offline and online evaluations on a real-world video recommendation system in WeChat Top Stories. The significant improvements in both video and tag related metrics confirm the effectiveness and robustness in real-world tag-enhanced video recommendation. Currently, GraphTR has been deployed on WeChat Top Stories for more than six months. The source codes are in https://github.com/lqfarmer/GraphTR.

Network embedding (NE) aims to embed the nodes of a network into a vector space, and serves as the bridge between machine learning and network data. Despite their widespread success, NE algorithms typically contain a large number of hyperparameters for preserving the various network properties, which must be carefully tuned in order to achieve satisfactory performance. Though automated machine learning (AutoML) has achieved promising results when applied to many types of data such as images and texts, network data poses great challenges to AutoML and remains largely ignored by the literature of AutoML. The biggest obstacle is the massive scale of real-world networks, along with the coupled node relationships that make any straightforward sampling strategy problematic. In this paper, we propose a novel framework, named AutoNE, to automatically optimize the hyperparameters of a NE algorithm on massive networks. In detail, we employ a multi-start random walk strategy to sample several small sub-networks, perform each trial of configuration selection on the sampled sub-network, and design a meta-leaner to transfer the knowledge about optimal hyperparameters from the sub-networks to the original massive network. The transferred meta-knowledge greatly reduces the number of trials required when predicting the optimal hyperparameters for the original network. Extensive experiments demonstrate that our framework can significantly outperform the existing methods, in that it needs less time and fewer trials to find the optimal hyperparameters.

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.

Network embedding aims to preserve vertex similarity in an embedding space. Existing approaches usually define the similarity by direct links or common neighborhoods between nodes, i.e. structural equivalence. However, vertexes which reside in different parts of the network may have similar roles or positions, i.e. regular equivalence, which is largely ignored by the literature of network embedding. Regular equivalence is defined in a recursive way that two regularly equivalent vertexes have network neighbors which are also regularly equivalent. Accordingly, we propose a new approach named Deep Recursive Network Embedding (DRNE) to learn network embeddings with regular equivalence. More specifically, we propose a layer normalized LSTM to represent each node by aggregating the representations of their neighborhoods in a recursive way. We theoretically prove that some popular and typical centrality measures which are consistent with regular equivalence are optimal solutions of our model. This is also demonstrated by empirical results that the learned node representations can well predict the indexes of regular equivalence and related centrality scores. Furthermore, the learned node representations can be directly used for end applications like structural role classification in networks, and the experimental results show that our method can consistently outperform centrality-based methods and other state-of-the-art network embedding methods.