Graph: Big Linked Data - graphbig/graphBIG GitHub Wiki

In the Big Data era, data are linked and form large graphs. Most traditional IT systems were designed for processing independent data, while analyses are mostly done by considering i.i.d. scenarios. Processing connected data has been a big challenge for Big Data analytics, which needs to consider traditional big data platforms for data that are more easily to be parallelized and novel graph computing platforms for data that are linked.

From the scientific aspect, Network as a new inter-disciplinary scientific field is emerging. Entities -- people, information, societies, nations, devices -- connect to each other and form all kinds of intertwined networks. Researchers from multiple disciplines -- electrical engineering, computer science, sociology, public health, economy, management, politics, laws, arts, physics, math, etc. -- are interacting with each other to build up common grounds of network science. Network theories are being formed for describing the dynamics, behaviors, and structures. A systematic mathematical formalism that enables predictions of network behavior and network interactions is also emerging. Trans-disciplinary approaches are usually required to lay the foundations of this science and to develop the requisite tools. Like 'Computer Science' was coined as an academic discilipine in the 1950s, we are now envisioning the emerging of 'Network Science'. Graph Computing is the "tool" for Network Science, for storing, processing, analyzing, and visualizing connected data.