Single-cell RNA-seq data has become a critical source in revealing cellular activities. However, the developing probing techniques and the fatal damages to the detected cells incur various kinds of noise, e.g. the batch effect and the absence of cellular correspondence between experimental groups. Therefore, many single-cell tasks are better modeled as generative rather than discriminative tasks, since, instead of the exact cell-wise ground truth, only the distribution of cellular profiles under a certain condition is measurable. Considering the highly nonlinear and complex associations between gene expressions, we developed scVDM, a latent diffusion model integrated with a transformer-based conditional denoiser to learn three different generative tasks in single-cell data, including conditional data generation, batch effect correction and drug perturbation prediction. The high dimensional transcriptomic data are firstly projected to the latent space through a conditional VAE and then the complicated relationships between latent dimensions are deeply exploited through self-attentions to generate realistic diffusion noise. Based on the evaluation of five real-world datasets, our method demonstrates outstanding performance through comprehensive experimental results in all generative tasks.

Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full combination space of genetic perturbations and cell types. Consequently, a bunch of computational models have been proposed to predict unseen combinations based on existing data. Among them, generative models, e.g. variational autoencoder and diffusion models, have the superiority in capturing the perturbed data distribution, but lack a biologically understandable foundation for generalization. On the other side of the spectrum, Gene Regulation Networks or gene pathway knowledge have been exploited for more reasonable generalization enhancement. Unfortunately, they do not reach a balanced processing of the two data modalities, leading to a degraded fitting ability. Hence, we propose a dual-stream architecture. Before the information from two modalities are merged, the sequencing data are learned with a generative model while three types of knowledge data are comprehensively processed with graph networks and a masked transformer, enforcing a deep understanding of single-modality data, respectively. The benchmark results show an approximate 20% reduction in terms of mean squared error, proving the effectiveness of the model.

Uncovering temporal dependency patterns behind event sequences plays a key role in predicting event types and event times. Recently, transformers based models have been used to describe point processes, such as the Transformer Hawkes Processes (THP models). However, existing THP models assume that data are collected in a central server and can be always seen during model training. Indeed, event sequence data are often located at different data centers which can not be shared directly due to the risk of privacy leakage. To this end, we combine in this paper the THP models with federated learning, enabling collaborative learning from a large amount of distributed event sequence data. Experiments show that our approach surpasses single data source training while preserving data privacy. For clients lacking certain types of event sequence data, our method performs much more stable than previous centralized training models.

Transcriptome-based survival modeling is a critical yet complicated task in cancer treatment due to the strong association between the prognosis of a patient and the progression of tumors based on heterogeneous molecular mechanisms. An effective prognosis model needs to consider the uncertainty that naturally stems from the high molecular heterogeneity together with numerous unforeseeable factors related to prognosis, and requires a perfect match between the selected covariates and the modeling approach of the joint distribution between covariates and right-censored survival time. Unfortunately, these necessary factors have not been fully considered in existing survival models. In this paper, we propose a novel conditional variational deep survival model (CVaDeS for short) for survival analysis. CVaDeS accurately predicts the risk values of patients and effectively distinguishes high-risk and low-risk individuals. Also, the proposed model CVaDeS can be effectively combined with Cox feature selection for survival analysis. Extensive experimental results demonstrate that the combination of Cox feature selection with CVaDeS significantly improves the predictive performance of the model, surpassing baseline models.

Graph neural networks (GNNs) are popularly used to analyze non-Euclidean graph data. Despite their successes, the design of graph neural networks requires heavy manual work and rich domain knowledge. Recently, neural architecture search algorithms are widely used to automatically design neural architectures for CNNs and RNNs. Inspired by the success of neural architecture search algorithms, we present a graph neural architecture search algorithm GraphNAS that enables automatic design of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS uses a recurrent network as the controller to generate variable-length strings that describe the architectures of graph neural networks, and trains the recurrent network with policy gradient to maximize the expected accuracy of the generated architectures on a validation data set. Moreover, based on GraphNAS, we design a new GraphNAS++ model using distributed neural architecture search. Compared with GraphNAS that generates and evaluates only one candidate architecture at each iteration, GraphNAS++ generates a mini-batch of candidate architectures and evaluates them in a distributed computing environment until convergence. Experiments on real-world datasets demonstrate that GraphNAS can design a novel network architecture that rivals the best human-invented architecture. Moreover, GraphNAS++ can speed up the design process at least five times by using the distributed training framework with GPUs.

Heterogeneous graphs are commonly used to describe networked data with multiple types of nodes and edges. Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for analyzing heterogeneous graphs. However, designing neural architectures of HGNNs requires extensive domain knowledge and time-consuming manual work. Recently, neural architecture search algorithms have become popular in automatically designing neural architectures for homogeneous graph neural networks. In this paper, we present a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which allows the automatic design of heterogeneous graph neural architectures. Specifically, HGNAS first designs a new search space based on existing popular HGNNs. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a given validation dataset. Moreover, we design a new method HGNAS++ to improve the efficiency of HGNAS by training the RNN controller within a generative adversarial learning framework. The basic idea of HGNAS++ is to embed a pairwise ranker into the reinforcement learning based architecture search algorithm. The pairwise ranker can be taken as a discriminator which selects more accurate architectures between pairs of candidate architectures. Then, the RNN controller can be updated more efficiently by only using a relatively small number of candidate architectures selected by the pairwise ranker. Experiments on real-world heterogeneous graph datasets show that HGNAS is capable of designing novel HGNNs that beat the best human-invented HGNNs. On the benchmark datasets, HGNAS++ improves HGNAS in terms of evaluation cost, with a reduction of 50% of the evaluated candidate architectures and a decrease of 24% in search time on average. As a byproduct, HGNAS++ can find sparse yet powerful neural architectures for HGNNs.

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean network data. Recently, there emerge a number of works to investigate the robustness of GNNs by adding adversarial noises into the graph topology, where gradient-based attacks are widely studied due to their inherent efficiency and high effectiveness. However, the gradient-based attacks often lead to sub-optimal results due to the discrete structure of graph data. To this end, we design a novel exploratory adversarial attack (termed as EpoAtk) to boost the gradient-based perturbations on graphs. The exploratory strategy in EpoAtk includes three phases, generation, evaluation and recombination, with the goal of sidesteping the possible misinformation that the maximal gradient provides. In experiments, EpoAtk is evaluated on benchmark datasets for the task of semi-supervised node classification in different attack settings. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art attacks with the same attack budgets.

Graph neural networks (GNNs) emerged recently as a powerful tool for analyzing non-Euclidean data such as social network data. Despite their success, the design of graph neural networks requires heavy manual work and domain knowledge. In this paper, we present a graph neural architecture search method (GraphNAS) that enables automatic design of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and trains the recurrent network with policy gradient to maximize the expected accuracy of the generated architectures on a validation data set. Furthermore, to improve the search efficiency of GraphNAS on big networks, GraphNAS restricts the search space from an entire architecture space to a sequential concatenation of the best search results built on each single architecture layer. Experiments on real-world datasets demonstrate that GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of validation set accuracy. Moreover, in a transfer learning task we observe that graph neural architectures designed by GraphNAS, when transferred to new datasets, still gain improvement in terms of prediction accuracy.

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.

Cross-lingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and run-time robustness. However, existing embedding alignment methods haven't considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the self-consistency and numerical stability of transformation. Experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines.

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i.i.d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.

Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.

Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.

Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.

Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

Network embedding plays a key role in network analysis, due to its ability to represent features of network structure in a low-dimensional Euclidean space, making it possible to directly utilize the of f-the-shelf mining techniques in a variety of analysis tasks. Although fruitful research papers on network embedding have sprung up in recent years, most of them neglect an important fact that nodes and edges in real-world networks are of diverse interests especially when the network contains little side information such as labels. To tackle this challenge, we propose a novel iWalk model to learn interest-aware network embedding in an unsupervised fashion. iWalk can automatically assign interest to nodes and edges based on network topology and construct custom paths navigated by assigned interest, then Skip-gram is used to learn network embedding from these paths. Sufficient experiments are conducted on different tasks and three typical datasets, the empirical results demonstrate that our model outperform the stat-of-art methods in most instances.

In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms in recommendation, for instance, social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.

Exploratory search is cumbersome with today's search engines, where a user aims to better understand complex concepts. Query expansions techniques have been widely used in exploratory search. However, query expansions often recommend queries that differ from the user's search intentions due to different contexts. Yet, many of users' needs could be addressed by asking people via popular Community Question Answering (CQA) services. In this paper, we investigate query expansion techniques for exploratory search using the resources of CQA to discover the user's search intentions. Specifically, we denote the explicit intuition as the subtopic that supports the user's exploratory task. We propose the method CqaQuExp to mine the subtopics, which mainly contains three subtask: Question retrieval, where we extract the questions and corresponding answers from CQA; subtopic mining, where we discover the subtopics based on the extracted information; Candidate concepts discovery, where we select the candidate concepts from the discovered subtopics for query expansion. Experimental results on real-world data from Yahoo! Answers demonstrate the effectiveness of the proposed methods.