Relation-aware Graph Attention Model With Adaptive Self-adversarial Training

This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. Existing message passing-based graph neural networks use edges either for graph traversal and/or selection of message encoding functions. Ignoring the edge semantics could have severe repercussions on the quality of embeddings, especially when dealing with two nodes having multiple relations. Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training. Although existing hard negative sampling techniques can identify challenging negative relationships for optimization, new techniques are required to control false negatives during training as false negatives could corrupt the learning process. To address these issues, first, we propose RelGNN -- a message passing-based heterogeneous graph attention model. In particular, RelGNN generates the states of different relations and leverages them along with the node states to weigh the messages. RelGNN also adopts a self-attention mechanism to balance the importance of attribute features and topological features for generating the final entity embeddings. Second, we introduce a parameter-free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling. ASA reduces the false-negative rate by leveraging positive relationships to effectively guide the identification of true negative samples. Our experimental evaluation demonstrates that RelGNN optimized by ASA for relationship prediction improves state-of-the-art performance across established benchmarks as well as on a real industrial dataset

Structured Workflow Management with Lotus Notes Release 4

In this paper we describe the design and implementation of workflow management applications on top of Notes Release 4. We elaborate on various design issues for Notes workflow applications and introduce Notes Release 4's native workflow concepts like agents, events, macros, LotusScript, OLE2 capabilities, and doclinks, which make Notes a powerful workflow tool. The idea of the paper is the use of the Workflow Reference Model of the Workflow Management Coalition [7] to define structured workflows, and execute these workflows through the exploitation of Notes Release 4's native workflow concepts. 1. Introduction Workflow management is currently a hot topic in industry and research. More and more products come out claiming workflow management functionality, and customers ask their vendors for this emerging technology. According to recent market forecasts the revenues for pure workflow engines will exceed $1 billion in the next couple of years. Groupware as the overall technology includes..