Community detection is a fundamental yet important task for characterizing and understanding the structure of attributed graphs. Existing methods mainly focus on the structural tightness and attribute similarity among nodes in a community. However, grouping numerous semantically homogeneous nodes will result in information cocoons and thus reduce the robustness of community structure and the efficiency of node collaboration in real-world applications, such as recommendation systems and collaboration networks. Since nodes with closer connections tend to be more similar, finding communities with dense structures and diverse attributes poses great challenges to mining latent relationships between the graph structure and attribute distribution. To our best knowledge, very little research has been conducted to address this challenge. In this article, we propose a novel three-view graph attention neural networks (TvGANN) model to formally address the attribute diversity aware community detection problem. TvGANN reveals correlations between the graph structure and attributes distribution from the perspective of node organization, attribute co-occurrence, and the node-attribute interaction. It effectively captures structural features and attributes distribution by feeding a structural network and an attribute co-occurrence network into graph attention modules through the encoder–decoder framework. It also learns heterogeneous information by feeding a network into a meta-node attention module. Then, it fuzes the three modules and clusters the embedding representations through a Student's t -distribution approach, which iteratively refines the clustering results. The experiments show that our method not only improves the quality in dense community detection but also performs efficiently for attributed graphs.

Labeling sufficient data for supervised learning remains an open challenge in social network analysis. An alternative is to collect self-labeled data, i.e. the data labeled by their owners. Emmery et al show that standard models can be trained and perform well on self-labeled data, suggesting the effectiveness of this approach. In this paper, we argue self-labeled data may not be representative of the population. Taking Twitter demographic prediction as an example, we show the popular FastText model standardly trained on self-labeled data does not generalize well on random testing samples. We then present a new learner DeFastText that aims to correct data bias using the kernel means matching technique. In experiment, we show it achieves lower generalization errors than FastText. This research raises an attention of the data bias problem when learning from self-labeled data in social network analysis.

The advancement of science and technology provides the possibility of personalized intelligent education. Representation learning of students’ behavior data is challenging because whether time sequences and interactive behaviors or the correlation between knowledge points and students carrying important information. Some researchers propose knowledge tracing to provide ideas for solving this dilemma. However, existing knowledge tracing methods are divided into machine learning and deep learning. Machine learning-based methods require manual feature extraction and a large amount of prior knowledge. Although deep learning-based methods can automatically extract features, most methods either only use the time series information of the data, or use the association between knowledge points. All the methods ignore the association between knowledge points and students. To fill this gap, we propose a Gated Heterogeneous Graph Convolutional Network (GHGCN) model. We utilize the encoder-decoder framework to predict student performance using the representations of nodes, which is learned from heterogeneous convolutional networks and gate recurrent unit. To validate the effectiveness of the proposed GHGCN model, we conduct the experiments on three public datasets: Simulated Data, Assistments 2009, and Assistments 2015. The results indicate that our method can achieve better performance compared with state-of-the-art algorithms.

Graph neural networks (GNNs) can be effectively applied to solve many real-world problems across widely diverse fields. Their success is inseparable from the message-passing mechanisms evolving over the years. However, current mechanisms treat all node features equally at the macro-level (node-level), and the optimal aggregation method has not yet been explored. In this paper, we propose a new GNN called Graph Decipher (GD), which transparentizes the message flows of node features from micro-level (feature-level) to global-level and boosts the performance on node classification tasks. Besides, to reduce the computational burden caused by investigating message-passing, only the relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category-oriented manner. Experiments on 10 node classification data sets show that GD achieves state-of-the-art performance while imposing a substantially lower computational cost. Additionally, since GD has the ability to explore the representative node attributes by category, it can also be applied to imbalanced node classification on multiclass graph data sets.

Dynamic graph neural networks (DGNNs) have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss, or continuous learning which involves heavy computation. In this study, we proposed a novel DGNN, sparse dynamic (Sparse-Dyn). It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding using snapshots which cause information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through structural neighborhoods and temporal dynamics. Since the fully connected attention conjunction is simplified, the computation cost is far lower than the current state-of-the-art. Link prediction experiments are conducted on both continuous and discrete graph data sets. By comparing several state-of-the-art graph embedding baselines, the experimental results demonstrate that Sparse-Dyn has a faster inference speed while having competitive performance.

Impression regulation plays an important role in various online ranking systems, e.g. , e-commerce ranking systems always need to achieve local commercial demands on some pre-labeled target items like fresh item cultivation and fraudulent item counteracting while maximizing its global revenue. However, local impression regulation may cause “butterfly effects” on the global scale, e.g. , in e-commerce, the price preference fluctuation in initial conditions (overpriced or underpriced items) may create a significantly different outcome, thus affecting shopping experience and bringing economic losses to platforms. To prevent “butterfly effects”, some researchers define their regulation objectives with global constraints, by using contextual bandit at the page-level that requires all items on one page sharing the same regulation action, which fails to conduct impression regulation on individual items. To address this problem, in this article, we propose a personalized impression regulation method that can directly makes regulation decisions for each user-item pair. Specifically, we model the regulation problem as a C onstrained D ual-level B andit (CDB) problem, where the local regulation action and reward signals are at the item-level while the global effect constraint on the platform impression can be calculated at the page-level only. To handle the asynchronous signals, we first expand the page-level constraint to the item-level and then derive the policy updating as a second-order cone optimization problem. Our CDB approaches the optimal policy by iteratively solving the optimization problem. Experiments are performed on both offline and online datasets, and the results, theoretically and empirically, demonstrate CDB outperforms state-of-the-art algorithms.

Irregularly, asynchronously and sparsely sampled multivariate time series (IASS-MTS) are characterized by sparse non-uniform time intervals between successive observations and different sampling rates amongst series. Those properties pose substantial challenges to mainstream machine learning models for learning complicated relations within and across IASS-MTS. This is because that most of the models assume that the time series in question are even, complete (fixed-dimensional features) and synchronous. To address these challenges, we present a novel time-aware Dual-Attention and Memory-Augmented Network (DAMA-Net). The proposed model can leverage both time irregularity, multi-sampling rates and global temporal patterns information inherent in IASS-MTS so as to learn more effective representations for improving prediction performance. Comprehensive experiments on real datasets show that the DAMA-Net outperforms the state-of-the-art methods in multivariate time series classification task.

Fair machine learning has become a significant research topic with broad societal impact. However, most fair learning methods require direct access to personal demographic data, which is increasingly restricted to use for protecting user privacy (e.g. by the EU General Data Protection Regulation). In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data. We assume this data is privately held by a third party, which can communicate with the data center (responsible for model development) without revealing the demographic information. We propose a principled approach to design fair learning methods under this framework, exemplify four methods and show they consistently outperform their existing counterparts in both fairness and accuracy across two real-world data sets. We theoretically analyze the framework, and prove it can learn models with high fairness or high accuracy, with their trade-offs balanced by a threshold variable.