Data-model matching, typically achieved through direct contact, is critical to digital markets. However, when data and models belong to different owners, the direct contact-based form faces some security threats, including data security, privacy disclosure, and model reverse engineering attacks. A natural question emerges: Can effective data-model matching be achieved without direct contact ? Previous methodologies can partially alleviate but not eliminate the necessity of direct contact between data and models, making security and privacy challenges persist throughout the matching process. In this article, our research findings indicate that, despite the essential differences between data and models, both can be represented using topological spaces. Therefore, we establish a unified metric of data complexity and model expressivity from a topological perspective. The unified metric satisfies three conditions toward contactless data-model matching. Then, we develop a contactless matching paradigm, circumventing the necessity for direct contact between data and models and addressing privacy and security concerns. Specifically, we use topological data analysis to generate the data complexity topological descriptors (DCTDs) and use topological simplification to generate the model expressivity topological descriptors (METDs). We compute the matching degree and return the matching result. Through theoretical proof and experimental analysis, we validate the feasibility of the proposed contactless data-model matching paradigm in real-world scenarios.

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.

As a typical latent factor model, Matrix Factorization (MF) has demonstrated its great effectiveness in recommender systems. Users and items are represented in a shared low-dimensional space so that the user preference can be modeled by linearly combining the item factor vector$V$using the user-specific coefficients$U$. From a generative model perspective,$U$and$V$are drawn from twoindependentGaussian distributions, which is not so faithful to the reality. Items are produced to maximally meet users’ requirements, which makes$U$and$V$strongly correlated. Meanwhile, the linear combination between$U$and$V$forces a bijection (one-to-one mapping), which thereby neglects the mutual correlation between the latent factors. In this paper, we address the upper drawbacks, and propose a new model, named Correlated Matrix Factorization (CMF). Technically, we apply Canonical Correlation Analysis (CCA) to map$U$and$V$into a new semantic space. Besides achieving the optimal fitting on the rating matrix, one component in each vector ($U$or$V$) is also tightly correlated with every single component in the other. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on four public datasets. Experimental results show that our approach achieves competitive performance on both prediction accuracy and efficiency compared with the current state of the art.

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in modeling such datasets is to discover proper denominators beneath the text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. We also impose auxiliary word embeddings to improve the quality of topics. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are hyperlinked networks of web pages, social networks of friends, and coauthor networks of publications. Experimental results show that our approach achieves significant improvements compared with the current state of the art.

The soaring of social media services has greatly propelled the prevalence of document networks. Rather than a set of plain texts, documents are nodes in graphs. An observable link connects the documents at its two ends, thus it implicitly reflects the semantic association between the document pair. Previous work assumes that only similar documents tend to be connected, which neglects the rich connective patterns in the topological structure. In this paper, we introduce a latent correlation factor to categorize the links into several categories, and each category corresponds to a unique kind of association. By fitting the data, the relational information (e.g., homophily and heterophily) can be comprehensively captured. By resorting to Canonical Correlation Analysis (CCA), we maximize the correlation between all pairs of linked documents. We propose a pure generative model and derive efficient learning algorithms based on the variational EM methods. Experiments on three different datasets demonstrate that the proposed model is competitive and usually better than the state-of-the-art baselines on both topic modeling and link prediction.

Internet information services have been greatly improved profiting from the growing performance of interest mining technology. Visual perceptual behaviours, a new hotspot of mining user's interests, have resulted in great gains in some typical Internet information services, e.g., information retrieval and recommendation. It is validated that combining the subjective visual perceptual behaviours with the objective contents can significantly improve these services' performance. However, the existing methods usually treat the contents and visual perceptual behaviours as two independent parts in the calculating process. The gain of visual perceptual behaviours has not been fully exploited. In this paper, we mainly aim at improving the gain of visual perceptual behaviour for text recommendation, by integrating the objective contents with subjective visual perceptual behaviours. We investigate the correlation between user's reading interests and records of real-time interaction on texts, and then design a real-time visual perceptual behaviour based method for text recommendation, which is able to: (1) build a joint interest model, called ViP-LDA (Visual Perceptual LDA), by integrating the user's visual perceptual behaviours into topic model; (2) make more accurate text recommendation based on ViP-LDA with feedback adjustment. Several experiments on a real data set are implemented to demonstrate the effectiveness of our method.

In this paper we aim at addressing the correlation between two critical factors in mobile social networks (MSNs): the social-relationship networking among users and the spatial mobility pattern of users. Specifically, we investigate the impact of users' spatial distribution on their social relationship formation in MSNs. Based on the geolocation data (check-in records) and social relation data of MSN users, we propose a model, called neighborhood-cardinality-based model (NCBM), to describe this impact by taking into account both the multiple home-points/hotspots property of spatial mobility and the long-tailed social relationship degree distribution of MSN users. We define a fundamental quantity for each user, i.e., the so-called neighborhood cardinality, to measure how many and how often other MSN users visit his nearby area with a given range. The core of NCBM is a principle: The probability that a user, say u, is followed by another user, say v, obeys a power law distribution of the neighborhood cardinality of user u. The proposed formation model is evaluated on two large check-in datasets: Brightkite and Gowalla. Our experimental results indicate that the proposed formation model provides a useful paradigm for capturing the correlation between MSN users' mobility patterns and social relationships.

Document network is a kind of intriguing dataset which provides both topical (texts) and topological (links) information. Most previous work assumes that documents closely linked with each other share common topics. However, the associations among documents are usually complex, which are not limited to the homophily (i.e., tendency to link to similar others). Actually, the heterophily (i.e., tendency to link to different others) is another pervasive phenomenon in social networks. In this paper, we introduce a new tool, called copula, to separately model the documents and links, so that different copula functions can be applied to capture different correlation patterns. In statistics, a copula is a powerful framework for explicitly modeling the dependence of random variables by separating the marginals and their correlations. Though widely used in Economics, copulas have not been paid enough attention to by researchers in machine learning field. Besides, to further capture the potential associations among the unconnected documents, we apply the tree-averaged copula instead of a single copula function. This improvement makes our model achieve better expressive power, and also more elegant in algebra. We derive efficient EM algorithms to estimate the model parameters, and evaluate the performance of our model on three different datasets. Experimental results show that our approach achieves significant improvements on both topic and link modeling compared with the current state of the art.

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in viably modeling such datasets is to discover proper denominators beneath the two different types of data, text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous latent factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. Logistic normal prior is also employed in modeling network to better capture the community correlations. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are namely hyperlinked networks of web pages, social networks of friends and coauthor networks of publications. Experimental results show that our approach achieves significant improvements on both topic modeling and community detection compared with the current state of the art. Meanwhile, our model is impressive in discovering correlations between extracted topics and communities.