Abstract: Social network analysis has become one of the hotspots in data mining research. Aggregating subgroups are important indicators for measuring the structure of social networks. The maximum clique structure is the most compact condensed subgroup in social networks. The study of the maximum clique problem has also become an important angle of social network analysis. With the development of big data, the massiveness of the nodes and the complexity of the edge structure in the graph put forward a higher requirement for solving the maximum clique problem. Therefore, a multi-strategy ant colony algorithm is proposed for solving the maximum clique problem first, the algorithm uses a multi-conditional selection strategy to expand the search space, increases the diversity of feasible solutions, and avoids falling into the local optimal solution. At the same time, a local search strategy is adopted to improve the accuracy and convergence speed of the algorithm; Then, the algorithm is implemented in parallel on the Spark distributed platform, which verifies the parallelism of the algorithm and improves the efficiency of the algorithm in handling large-scale community networks.

Key words: maximum clique, Spark, ant colony algorithm, ants choosing strategy, local improvement strategy