Transduction on Directed Graphs via Absorbing Random Walks
This addresses classification on directed graphs, a common but underexplored scenario in real-world applications, offering an incremental improvement over existing methods that often ignore or symmetrize directionality.
The paper tackles graph-based transductive classification on directed graphs by proposing a novel random walk approach using absorbing Markov chains, which maximizes accumulated expected visits from unlabeled states. Empirically, it performs competitively against state-of-the-art methods across various applications.
In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications. Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states. Our algorithm is simple, easy to implement, and works with large-scale graphs. In particular, it is capable of preserving the graph structure even when the input graph is sparse and changes over time, as well as retaining weak signals presented in the directed edges. We present its intimate connections to a number of existing methods, including graph kernels, graph Laplacian based methods, and interestingly, spanning forest of graphs. Its computational complexity and the generalization error are also studied. Empirically our algorithm is systematically evaluated on a wide range of applications, where it has shown to perform competitively comparing to a suite of state-of-the-art methods.