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Due to the growing need to process large graph and network datasets created by modern applications, recent years have witnessed a surging interest in developing big graph platforms. Tens of such big graph systems have already been developed, but there lacks a systematic categorization and comparison of these systems. This article provides a timely and comprehensive survey of existing big graph systems, and summarizes their key ideas and technical contributions from various aspects. In addition to the popular vertex-centric systems which espouse a think-like-a-vertex paradigm for developing parallel graph applications, this survey also covers other programming and computation models, contrasts those against each other, and provides a vision for the future research on big graph analytics platforms. This survey aims to help readers get a systematic picture of the landscape of recent big graph systems, focusing not just on the systems themselves, but also on the key innovations and design philosophies underlying them. [ABSTRACT FROM AUTHOR]