Graph Retrieval has witnessed continued interest and progress in the past few years. In thisreport, we focus on neural network based approaches for Graph matching and retrieving similargraphs from a corpus of graphs. We explore methods which can soft predict the similaritybetween two graphs. Later, we gauge the power of a particular baseline (Shortest Path Kernel)and try to model it in our product graph random walks setting while making it more generalised.
翻译:Retreival图在过去几年中一直受到关注并取得了进步。在本报告中,我们侧重于基于神经网络的图表匹配和从图集中获取类似图谱的方法。我们探索了可以软预测两个图集相似性的方法。后来,我们测量了特定基线(Shortest Path Kernel)的力量,并试图在产品图表随机行走设置中进行模型化,同时将其更加概括化。