BACKGROUND: A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases. RESULTS: To further explore the potential functional relationships between multi-hop proteins in PPI networks, this paper proposes SEGT-GO, a Graph Transformer method based on PPI multi-hop neighborhood Serialization and Explainable artificial intelligence for large-scale multispecies protein function prediction. The multi-hop neighborhood serialization maps multi-hop information in the PPI Network into serialized feature embeddings, enabling the Graph Transformer to learn deeper functional features within the PPI Network. Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance. CONCLUSIONS: Compared to the advanced network method DeepGraphGO, SEGT-GO achieves more competitive results in standard large-scale datasets and superior results on small ones, validating its ability to extract functional information from deep proteins. Furthermore, SEGT-GO achieves superior results in cross-species learning and prediction of the functions of unseen proteins, further proving the method's strong generalization.

The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous graphs, and in which each layer can only aggregate the information of one-hop neighbors. Stacking multilayer networks introduces considerable noise and easily leads to over smoothing. We propose here a multihop heterogeneous neighborhood information fusion graph representation learning method (MHNF). Specifically, we propose a hybrid metapath autonomous extraction model to efficiently extract multihop hybrid neighbors. Then, we formulate a hop-level heterogeneous information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information. In this fashion, this paper solves the problem of aggregating multihop neighborhood information and learning hybrid metapaths for target tasks. This mitigates the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information present at different levels. Experimental results on real datasets show that MHNF achieves the best or competitive performance against state-of-the-art baselines with only a fraction of 1/10$\sim$1/100 parameters and computational budgets. Our code is publicly available athttps://github.com/PHD-lanyu/MHNF

Cache prefetching technology has become the mainstream data access optimization strategy in the Industrial Intelligent Systems (IIS) and the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships. Therefore, researchers have to make a choice between spatial locality and temporal locality to ensure an acceptable computational complexity. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art and traditional methods, our algorithm achieves an average increase in the cache hit rate of 5%–20% on the MSR, VDI-LUN, and KC data set, which in turn reduces the number of data I/O accesses by 30%–60%.

The private chain-based Internet-of-Things (IoT) system ensures the security of cross-organizational data sharing. As a widely used consensus model in private chains, the leader-based state-machine replication (SMR) model meets the performance bottleneck in IoT blockchain applications, where nontransactional sensor data are generated on a scale. We analyzed IoT private chain systems and found that the leader maintains too many connections due to high latency and client request frequency, which results in lower consensus performance and efficiency. To meet this challenge, we propose a novel solution for maintaining low request latency and high transactions per second (TPS): replicate nontransactional data by followers and confirm by the leader to achieve nonconfliction SMR, rather than all by the leader. Our solution, named leader confirmation replication (LCR), uses the newly proposed future log and confirmation signal to achieve nontransactional data replication on the followers, thereby reducing the leader’s network traffic and the request latency of transactional data. In addition, the generation replication strategy is designed to ensure the reliability and consistency of LCR when meeting membership changes. We evaluated LCR with various cluster sizes and network latencies. The experimental results show that in ms-network latency (2–30) environments, the TPS of LCR is 1.4X-1.9X higher than Raft, the transactional data response time is reduced by 40%–60%, and the network traffic is reduced by 20%–30% with acceptable network traffic and CPU cost on the followers. In addition, LCR shows high portability and availability since it does not change the number of leaders or the election process.

With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this article, we propose a method of hierarchical unsupervised network alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an unsupervised network alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic data sets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.