The search for entities is the most common search behavior on the Web, especially in social media communities where entities (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media entities separately, we are investigating in this paper a unified, multi-level, and correlative entity graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as “entities” and all of these applications as “entity search” problem which searches for entities with different types. We first construct a multi-level graph which organizes the heterogeneous entities into multiple levels, with one type of entities as vertices in each level. The edges between graphs pairwisely connect the entities weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intra-relations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different entity types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate entity search as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified entity graph for various social media applications on million-scale real-world dataset.