Exploiting the Block Structure of Link Graph for Efficient Similarity Computation

In many real-world domains, link graph is one of the most effective ways to model the relationships between objects. Measuring the similarity of objects in a link graph is studied by many researchers, but an effective and efficient method is still expected. Based on our observation of link graphs from real domains, we find the block structure naturally exists. We propose an algorithm called BlockSimRank , which partitions the link graph into blocks, and obtains similarity of each node-pair in the graph efficiently. Our method is based on random walk on two-layer model, with time complexity as low as O (n 4/3) and less memory need. Experiments show that the accuracy of BlockSimRank is acceptable when the time cost is the lowest.