Graph matching is widely used in reality, of which subgraph isomorphic matching is a research hotspot and has important scientific significance and practical value. Most existing subgraph isomorphism algorithms build constraints based on neighbor relationships, ignoring the local neighborhood information of nodes. In order to solve the problem, a subgraph isomorphism matching algorithm based on neighbor information aggregation was proposed. Firstly, the aggregated local neighborhood information of the nodes was obtained by importing the graph attributes and structure into the improved graph convolutional neural network to perform the representation learning of feature vector. Then, the efficiency of the algorithm was improved by optimizing the matching order according to the characteristics such as the label and degree of the graph. Finally, the Constraint Satisfaction Problem (CSP) model of subgraph isomorphism was established by combining the obtained feature vector and the optimized matching order with the search algorithm, and the model was solved by using the CSP backtracking algorithm. Experimental results show that the proposed algorithm significantly improves the solving efficiency of subgraph isomorphism compared with the traditional tree search algorithm and constraint solving algorithm.

Large amount of uncertainty in PPI network and the incompleteness of the known protein complex data add inaccuracy to the methods only considering the topological structural information to search or performing supervised learning to the known complex data. In order to solve the problem, a search method called XGBoost model for Predicting protein complex (XGBP) was proposed. Firstly, feature extraction was performed based on the topological structural information of complexes. Then, the extracted features were trained by XGBoost model. Finally, a mapping relationship between features and protein complexes was constructed by combining topological structural information and supervised learning method, in order to improve the accuracy of protein complex prediction. Comparisons were performed with eight popular unsupervised algorithms: Markov CLustering (MCL), Clustering based on Maximal Clique (CMC), Core-Attachment based method (COACH), Fast Hierarchical clustering algorithm for functional modules discovery in Protein Interaction (HC-PIN), Cluster with Overlapping Neighborhood Expansion (ClusterONE), Molecular COmplex DEtection (MCODE), Detecting Complex based on Uncertain graph model (DCU), Weighted COACH （WCOACH）； and three supervisedmethods Bayesian Network (BN), Support Vector Machine (SVM), Regression Model (RM). The results show that the proposed algorithm has good performance in terms of precision, sensitivity and F-measure.