  Graph representation learning (a.k.a. network embedding) is a significant
topic of network analysis, due to its effectiveness to support various graph
inference tasks. In this paper, we study the representation learning with
multiple information sources in attributed graphs. Recent studies usually focus
on several specific sources (e.g., high-order proximity and node attributes)
but few of them can be extended to incorporate other available sources not
specified. In addition, most existing methods assume that all the integrated
sources share consistent latent features but may ignore the possible
inconsistency among them, lacking the required robustness. To address these
issues, we propose a novel adaptive hybrid graph representation (AHGR) method
from a view of graph reweighting, where each information source is formulated
as a corresponding auxiliary graph, enabling AHGR to integrate arbitrary
available information sources. Moreover, a new transition relation among the
reweighted graphs is then introduced to perceive and resist the possible
inconsistency among multiple sources, enhancing the robustness of AHGR. We
verify the effectiveness of AHGR on a series of synthetic and real attributed
graphs, where it presents superior performance over other baselines.
