Graph Algorithm Deployment and Practices on the DataFun Security Spark Cluster

The DataFun Security cluster is a Spark environment designed for secure, large‑scale data computation, making it an attractive platform for graph learning tasks that can be categorized as inductive or transductive.

Inductive algorithm deployment focuses on methods that do not require full graph deployment. It discusses running PyTorch on Spark via RDDBarrier and DistributedDataParallel, as well as using the PyG framework with various data‑loading strategies (full load, driver‑side neighbor loading, and block manager‑based sampling). Graph partitioning techniques, including community‑based cuts and the use of BlockManager for a simple distributed database, are also described.

Transductive algorithm deployment emphasizes methods that need the entire graph, such as GraphX with Pregel and GAS models, and details optimizations like using float precision, sparse vectors, and timely unpersisting. It also covers adding parameter learning capabilities to GraphX and leveraging block manager or PyTorch for parameter synchronization.

The article reviews common graph algorithms: matrix‑factorization methods (DeepWalk, Node2Vec), encoder‑based approaches (LINE, SDNE), and deep neural network models (GCN, GraphSAGE, GAT, GIN). It explains their theoretical foundations, how they handle first‑order and second‑order similarities, and the challenges of scaling them on large graphs.

Model training strategies such as folded training, AUC hypothesis testing, and IV evaluation are introduced to assess effectiveness and downstream utility. The discussion concludes with a summary of typical use cases—pattern matching, label propagation, attribute propagation, and centrality computation—and outlooks on future improvements.