Scalable Explanation of Inferences on Large Graphs Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it is desirable to help the endusers understand the inference outcomes. Different from deep or high dimensional parametric models, the lack of interpretability in graphical models is due to the cyclic and longrange dependencies and the byzantine inference procedures. Prior works did not tackle cycles and make the inferences interpretable. We formulate the explanation of probabilistic inferences as a constrained crossentropy minimization problem to find simple subgraphs that faithfully approximate the inferences. We prove that the optimization is NPhard, while the objective is not monotonic and submodular to guarantee efficient greedy approximation. We propose a beam search algorithm to find trees to enhance the explanation interpretability and diversity. To allow efficient search on large and dense graphs without hurting faithfulness, we further propose parallelization and a pruning strategy. We demonstrate superior performance on four networks from distinct applications, comparing favorably to other explanation methods, including LIME.

Discovering Robustly Connected Subgraphs with Simple Descriptions We study the problem of discovering robustly connected subgraphs that have simple descriptions. Our aim is, hence, to discover vertex sets which not only a) induce a subgraph that is difficult to fragment into disconnected components, but also b) can be selected from the entire graph using just a simple conjunctive query on their vertex attributes. Since many subgraphs do not have such a simple logical description, first mining robust subgraphs and posthoc discovering their description leads to suboptimal results. Instead, we propose to optimise over describable subgraphs only. To do so efficiently we propose a nonredundant iterative deepening approach, which we equip with a lineartime tight optimistic estimator that allows pruning large parts of the search space. Extensive empirical evaluation shows that our method can handle large realworld graphs, and discovers easily interpretable and meaningful subgraphs.