Dolha  an efficient and exact data structure for streaming graphs A streaming graph is a graph formed by a sequence of incoming edges with time stamps. Unlike the static graphs, the streaming graph is highly dynamic and timerelated. Streaming graphs in the real world, which are of the high volume and velocity, can be challenging to the classic graph data structures: data of internet traffic, social network communication, and financial transections, etc. The traditional graph storage models like the adjacency matrix and the adjacency list are no longer sufficient for the large amount data and high frequency updates. And most the streaming graph structures are only supports the specific graph algorithms. Here a new data structure is presented to meet the challenge: a double orthogonal list in hash table (Dolha) as a high speed and high memory efficiency graph structure. Dolha has constant time cost for single edge processing, and nearlinear space cost. Moreover, time cost for neighborhood queries in Dolha is linear, which enables it to support most algorithms of graphs without extra cost. A persistent structure based on Dolha is also presented, to handle the sliding window update and time related queries.

Distributed Triangle Counting Algorithms in Simple Graph Stream Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, onepass streaming algorithm for counting global and local triangles has been widely studied, and most researches focus on the singlemachine streaming algorithm in a u0027offlinebatch processingu0027 mode. However, researches on distributed online algorithm on multiple machines are still in its infancy, and this stage has not been thoroughly studied. In this paper, we investigate the triangle counting problem in largescale simple undirected graphs whose edges arrive as a stream. We propose two distributed online streaming algorithms to estimate the global number of triangles, which are based on the current best performance samplingbased streaming algorithm. We mainly realize the reasonable partition of the graph stream, so that each worker independently estimates the number of triangles in a subgraph of the graph stream. Experimental results show that our algorithms reduce the estimation error and are several times more accurate than stateoftheart streaming algorithms.