Fast algorithms to improve fair information access in networks

• URL: http://arxiv.org/abs/2409.03127v2
• Date: Wed, 19 Feb 2025 18:36:16 GMT
• Title: Fast algorithms to improve fair information access in networks
• Authors: Dennis Robert Windham, Caroline J. Wendt, Alex Crane, Madelyn J Warr, Freda Shi, Sorelle A. Friedler, Blair D. Sullivan, Aaron Clauset,
• Abstract summary: We consider the problem of selecting $k$ seed nodes in a network to maximize the minimum probability of activation.<n>We design and evaluate a suite of $10$ new scalable algorithms which crucially do not require probability estimation.<n>We provide a new benchmark corpus of $174$ networks drawn from $6$ domains.
• Score: 8.174758604182268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of selecting $k$ seed nodes in a network to maximize the minimum probability of activation under an independent cascade beginning at these seeds. The motivation is to promote fairness by ensuring that even the least advantaged members of the network have good access to information. Our problem can be viewed as a variant of the classic influence maximization objective, but it appears somewhat more difficult to solve: only heuristics are known. Moreover, the scalability of these methods is sharply constrained by the need to repeatedly estimate access probabilities. We design and evaluate a suite of $10$ new scalable algorithms which crucially do not require probability estimation. To facilitate comparison with the state-of-the-art, we make three more contributions which may be of broader interest. We introduce a principled method of selecting a pairwise information transmission parameter used in experimental evaluations, as well as a new performance metric which allows for comparison of algorithms across a range of values for the parameter $k$. Finally, we provide a new benchmark corpus of $174$ networks drawn from $6$ domains. Our algorithms retain most of the performance of the state-of-the-art while reducing running time by orders of magnitude. Specifically, a meta-learner approach is on average only $20\%$ less effective than the state-of-the-art on held-out data, but about $75-130$ times faster. Further, the meta-learner's performance exceeds the state-of the-art on about $20\%$ of networks, and the magnitude of its running time advantage is maintained on much larger networks.

Related papers

• Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization [15.179519413549086]
  We introduce a new accelerated $ell_p$ steepest descent algorithm, called Stacey, to handle non-Euclidean smooth optimization tasks.<n>In addition to providing theoretical guarantees for the foundations of our algorithm, we empirically compare our approach against popular methods.
  arXiv  Detail & Related papers  (2025-06-07T00:47:07Z)
• Online Fair Division for Personalized $2$-Value Instances [51.278096593080456]
  We study an online fair division setting, where goods arrive one at a time and there is a fixed set of $n$ agents.<n>Once a good appears, the value each agent has for it is revealed and it must be allocated immediately and irrevocably to one of the agents.<n>We show how to obtain worst case guarantees with respect to well-known fairness notions.
  arXiv  Detail & Related papers  (2025-05-28T09:48:16Z)
• Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection [52.716143424856185]
  We propose LiMA (Less input is More faithful for Attribution), which reformulates the attribution of important regions as an optimization problem for submodular subset selection. LiMA identifies both the most and least important samples while ensuring an optimal attribution boundary that minimizes errors. Our method also outperforms the greedy search in attribution efficiency, being 1.6 times faster.
  arXiv  Detail & Related papers  (2025-04-01T06:58:15Z)
• Learning State-Augmented Policies for Information Routing in Communication Networks [92.59624401684083]
  We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
  arXiv  Detail & Related papers  (2023-09-30T04:34:25Z)
• A Fast Algorithm for Moderating Critical Nodes via Edge Removal [19.130541561303293]
  We study the problem of removing $k$ edges from a network to minimize the information centrality of a target node. We propose three approximation greedy algorithms using novel techniques such as random walk-based Schur complement approximation and fast sum estimation. To complement our theoretical analysis, we conduct a comprehensive set of experiments on synthetic and real networks with over one million nodes.
  arXiv  Detail & Related papers  (2023-09-09T13:54:34Z)
• Cooperative Thresholded Lasso for Sparse Linear Bandit [6.52540785559241]
  We present a novel approach to address the multi-agent sparse contextual linear bandit problem. It is first algorithm that tackles row-wise distributed data in sparse linear bandits. It is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret.
  arXiv  Detail & Related papers  (2023-05-30T16:05:44Z)
• ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms [5.478671305092084]
  We introduce ParlayANN, a library of deterministic and parallel graph-based approximate nearest neighbor search algorithms. We develop novel parallel implementations for four state-of-the-art graph-based ANNS algorithms that scale to billion-scale datasets.
  arXiv  Detail & Related papers  (2023-05-07T19:28:23Z)
• Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy [6.170898159041277]
  We present an algorithm for minimizing an objective with hard-to-compute gradients by using a related, easier-to-access function as a proxy. Our algorithm guarantees convergence at a rate matching the gradient descent on a $delta$-smooth objective. Our algorithm has many potential applications in machine learning, and provides a principled means of leveraging synthetic data, physics simulators, mixed public and private data, and more.
  arXiv  Detail & Related papers  (2023-02-07T15:50:49Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Communication-Efficient Adam-Type Algorithms for Distributed Data Mining [93.50424502011626]
  We propose a class of novel distributed Adam-type algorithms (emphi.e., SketchedAMSGrad) utilizing sketching. Our new algorithm achieves a fast convergence rate of $O(frac1sqrtnT + frac1(k/d)2 T)$ with the communication cost of $O(k log(d))$ at each iteration.
  arXiv  Detail & Related papers  (2022-10-14T01:42:05Z)
• Learning with Differentiable Algorithms [6.47243430672461]
  This thesis explores combining classic algorithms and machine learning systems like neural networks. The thesis formalizes the idea of algorithmic supervision, which allows a neural network to learn from or in conjunction with an algorithm. In addition, this thesis proposes differentiable algorithms, such as differentiable sorting networks, differentiable sorting gates, and differentiable logic gate networks.
  arXiv  Detail & Related papers  (2022-09-01T17:30:00Z)
• FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification [4.909402570564468]
  We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE) Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. Our method is substantially more effective in a setting with a high pruning rate (99%)
  arXiv  Detail & Related papers  (2022-07-22T05:44:03Z)
• On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets [2.3478438171452014]
  We focus on image classification problems with a few labeled examples per class and improve data efficiency by using an ensemble of relatively small networks. We show that ensembling relatively shallow networks is a simple yet effective technique that is generally better than current state-of-the-art approaches for learning from small datasets.
  arXiv  Detail & Related papers  (2021-11-29T12:34:49Z)
• DAAS: Differentiable Architecture and Augmentation Policy Search [107.53318939844422]
  This work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them. Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
  arXiv  Detail & Related papers  (2021-09-30T17:15:17Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• Learning Structures for Deep Neural Networks [99.8331363309895]
  We propose to adopt the efficient coding principle, rooted in information theory and developed in computational neuroscience. We show that sparse coding can effectively maximize the entropy of the output signals. Our experiments on a public image classification dataset demonstrate that using the structure learned from scratch by our proposed algorithm, one can achieve a classification accuracy comparable to the best expert-designed structure.
  arXiv  Detail & Related papers  (2021-05-27T12:27:24Z)
• Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks [50.684661759340145]
  Firefly neural architecture descent is a general framework for progressively and dynamically growing neural networks. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures. In particular, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-17T04:47:18Z)
• Network Support for High-performance Distributed Machine Learning [17.919773898228716]
  We propose a system model that captures both learning nodes (that perform computations) and information nodes (that provide data) We then formulate the problem of selecting (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of iterations to perform. We devise an algorithm, named DoubleClimb, that can find a 1+1/|I|-competitive solution with cubic worst-case complexity.
  arXiv  Detail & Related papers  (2021-02-05T19:38:57Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Byzantine-Resilient Non-Convex Stochastic Gradient Descent [61.6382287971982]
  adversary-resilient distributed optimization, in which. machines can independently compute gradients, and cooperate. Our algorithm is based on a new concentration technique, and its sample complexity. It is very practical: it improves upon the performance of all prior methods when no. setting machines are present.
  arXiv  Detail & Related papers  (2020-12-28T17:19:32Z)
• Progressive Skeletonization: Trimming more fat from a network at initialization [76.11947969140608]
  We propose an objective to find a skeletonized network with maximum connection sensitivity. We then propose two approximate procedures to maximize our objective. Our approach provides remarkably improved performance on higher pruning levels.
  arXiv  Detail & Related papers  (2020-06-16T11:32:47Z)
• Towards Deep Learning Models Resistant to Large Perturbations [0.0]
  Adversarial robustness has proven to be a required property of machine learning algorithms. We show that the well-established algorithm called "adversarial training" fails to train a deep neural network given a large, but reasonable, perturbation magnitude.
  arXiv  Detail & Related papers  (2020-03-30T12:03:09Z)
• Ranking a set of objects: a graph based least-square approach [70.7866286425868]
  We consider the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of equal workers. We propose a class of non-adaptive ranking algorithms that rely on a least-squares intrinsic optimization criterion for the estimation of qualities.
  arXiv  Detail & Related papers  (2020-02-26T16:19:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.