Here we publish some datasets and codes used by our papers. You could freely use them in your academic research with proper citation to the original papers. We reserve the rights of any commercial use.
Real-world graphs are not always publicly available or sometimes do not meet specific research requirements. These challenges call for generating synthetic networks that follow properties of the real-world networks. Barabási-Albert (BA) is a well-known model for generating scale-free graphs, i.e graphs with power-law degree distribution. In BA model, the network is generated through an iterative stochastic process called preferential attachment. Although BA is highly demanded, due to the inherent complexity of the preferential attachment, this model cannot be scaled to generate billion-node graphs. In this paper, we propose ROLL-tree, a fast in-memory roulette wheel data structure that accelerates the BA network generation process by exploiting the statistical behaviors of the underlying growth model. Our proposed method has the following properties: (a) Fast: It performs +1000 times faster than the state-of-the-art on a single node PC; (b) Exact: It strictly follows the BA model, using an efficient data structure instead of approximation techniques; (c) Generalizable: It can be adapted for other “rich-get-richer” stochastic growth models. Our extensive experiments prove that ROLL-tree can effectively accelerate graph-generation through the preferential attachment process. On a commodity single processor machine, for example, ROLL-tree generates a scale-free graph of 1.1 billion nodes and 6.6 billion edges (the size of Yahoo’s Webgraph) in 62 minutes while the state-of-the-art (SA) takes about four years on the same machine.
With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose SpiderMine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1−ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a probabilistic mining framework to find the top-k largest patterns by (i) identifying an affordable set of promising growth paths toward large patterns, (ii) generating large patterns with much lower combinatorial complexity, and finally (iii) greatly reducing the cost of graph isomorphism tests with a new graph pattern representation by a multi-set of spiders. Extensive experimental studies on both synthetic and real data sets show that our algorithm outperforms existing methods.