Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.

Although various methods are proposed to generate music accompaniment tracks according to the main music track, they suffer from the problems of modeling music dependencies and representing music data, therefore, generating high-quality music accompaniment remains a challenging task.To address this issue, we propose a multi-track sequential convolutional neural network (MuseCNN) to generate accompaniment tracks corresponding to the main music track.Inspired by the similarity between pianoroll representation and pictures, we transform the pianoroll data into a two-channel music representation matrix and feed it into the convolutional neural network (CNN).Using a hierarchical loss function, we integrate all music tracks to maintain the coherence and harmony of the music.Based on the three-level loss design and multiple CNNs, the problem of modeling music dependencies can be solved.The experimental results indicate that our multi-CNN model can effectively learn complex music dependencies and generate harmonious longsequence polyphonic music.

For the past decades, the subgraph similarity search over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely subgraph similarity search under aggregated neighbor difference semantics (S 3 AND), which identifies subgraphs g in a data graph G that are similar to a given query graph q by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the S 3 AND problem, we design two effective pruning methods, keyword set and aggregated neighbor difference lower bound pruning , which rule out false alarms of candidate vertices/subgraphs to reduce the S 3 AND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient S 3 AND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our S 3 AND approach over both real and synthetic graphs under various parameter settings.

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI1model achieved superior performance to state-of-the-art models on DDI prediction.