We study the min-cost seed selection problem in online social networks, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the state-of-the-art algorithms both theoretically and experimentally. Under the case where the users have heterogeneous costs, our algorithms are the first bi-criteria approximation algorithms with polynomial running time and provable logarithmic performance bounds using a general contagion model. Under the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.

We consider the edge uncertainty in an undirected graph and study the k-median (resp. k-center) problems, where the goal is to partition the graph nodes into k clusters such that the average (resp. minimum) connection probability between each node and its cluster's center is maximized. We analyze the hardness of these problems, and propose algorithms that provide considerably improved approximation guarantees than the existing studies do. Specifically, our algorithms offer (1 − 1/e)-approximations for the k-median problem and (OPT c k)-approximations for the k-center problem, where OPT c k is the optimal objective function value for k-center. In addition, our algorithms incorporate several non-trivial optimizations that significantly enhance their practical efficiency. Extensive experimental results demonstrate that our algorithms considerably outperform the existing methods on both computation efficiency and the quality of clustering results.

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As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model. Recently, many studies have shown that such biases are also imbibed in Graph Neural Network (GNN) models if the input graph is biased. In this work, we aim to mitigate the bias learned by GNNs through modifying the input graph. To that end, we propose FairMod, a Fair Graph Modification methodology with three formulations: the Global Fairness Optimization (GFO), Community Fairness Optimization (CFO), and Fair Edge Weighting (FEW) models. Our proposed models perform either microscopic or macroscopic edits to the input graph while training GNNs and learn node embeddings that are both accurate and fair under the context of link recommendations. We demonstrate the effectiveness of our approach on four real world datasets and show that we can improve the recommendation fairness by several factors at negligible cost to link prediction accuracy.CCS Concepts: • Computing methodologies → Neural networks.