  Graph Neural Networks (GNNs) have exhibited impressive performance in many
graph learning tasks. Nevertheless, the performance of GNNs can deteriorate
when the input graph data suffer from weak information, i.e., incomplete
structure, incomplete features, and insufficient labels. Most prior studies,
which attempt to learn from the graph data with a specific type of weak
information, are far from effective in dealing with the scenario where diverse
data deficiencies exist and mutually affect each other. To fill the gap, in
this paper, we aim to develop an effective and principled approach to the
problem of graph learning with weak information (GLWI). Based on the findings
from our empirical analysis, we derive two design focal points for solving the
problem of GLWI, i.e., enabling long-range propagation in GNNs and allowing
information propagation to those stray nodes isolated from the largest
connected component. Accordingly, we propose D$^2$PT, a dual-channel GNN
framework that performs long-range information propagation not only on the
input graph with incomplete structure, but also on a global graph that encodes
global semantic similarities. We further develop a prototype contrastive
alignment algorithm that aligns the class-level prototypes learned from two
channels, such that the two different information propagation processes can
mutually benefit from each other and the finally learned model can well handle
the GLWI problem. Extensive experiments on eight real-world benchmark datasets
demonstrate the effectiveness and efficiency of our proposed methods in various
GLWI scenarios.
