Detecting resilient structures in stochastic networks: A two-stage stochastic optimization approach

We propose a two-stage stochastic programming framework for designing or identifying "resilient," or "reparable" structures in graphs whose topology may undergo a stochastic transformation. The reparability of a subgraph satisfying a given property is defined in terms of a budget constraint, which allows for a prescribed number of vertices to be added to or removed from the subgraph so as to restore its structural properties after the observation of random changes to the graph's set of edges. A two-stage stochastic programming model is formulated and is shown to be N P -complete for a broad range of graph-theoretical properties that the resilient subgraph is required to satisfy. A general combinatorial branch-and-bound algorithm is developed, and its computational performance is illustrated on the example of a two-stage stochastic maximum clique problem. © 2016 Wiley Periodicals, Inc. NETWORKS, 201

A heuristic approach for the distance-based critical node detection problem in complex networks

The distance-based critical node problem involves identifying a subset of nodes in a network whose removal minimises a pre-defined distance-based connectivity measure. Having the classical critical node problem as a special case, the distance-based critical node problem is computationally challenging. In this article, we study the distance-based critical node problem from a heuristic algorithm perspective. We consider the distance-based connectivity objective whose goal is to minimise the number of node pairs connected by a path of length at most k, subject to budgetary constraints. We propose a centrality based heuristic which combines a backbone-based crossover procedure to generate good offspring solutions and a centrality-based neighbourhood search to improve the solution. Extensive computational experiments on real-world and synthetic graphs show the effectiveness of the developed heuristic in generating good solutions when compared to exact solution. Our empirical results also provide useful insights for future algorithm development

Multitask deep learning for native language identification

Identifying the native language of a person by their text written in English (L1 identification) plays an important role in such tasks as authorship profiling and identification. With the current proliferation of misinformation in social media, these methods are especially topical. Most studies in this field have focused on the development of supervised classification algorithms, that are trained on a single L1 dataset. Although multiple labeled datasets are available for L1 identification, they contain texts authored by speakers of different languages and do not completely overlap. Current approaches achieve high accuracy on available datasets, but this is attained by training an individual classifier for each dataset. Studies show that joint training of multiple classifiers on different datasets can result in sharing information between the classifiers, leading to an increase in the accuracy of both tasks. In this study, we develop a novel deep neural network (DNN) architecture for L1 classification; it is based on an adversarial multitask learning method that integrates shared knowledge from multiple L1 datasets. We propose several variants of the architecture and rigorously evaluate their performance on multiple datasets. Our results indicate the proposed multitask architecture is more efficient in terms of classification accuracy than previously proposed methods.peerReviewe