This paper defines resilience in knowledge graph embeddings and surveys existing works.
problem Challenges in knowledge graph embedding models, including noise, missing information, distribution shift, and adversarial attacks.
method Unified definition of resilience, formalization in the context of knowledge graphs, systematic survey of existing works.
result Most existing works focus on robustness, leaving other aspects of resilience unexplored.
New method avoids curse of dimensionality in structured density estimation.
problem Estimating multivariate density with Markov graph constraints.
method Introduces 'graph resilience' to control sample complexity.
result Avoids curse of dimensionality under Markov conditions.
Graph conformal prediction predicts future power outages with high confidence.
problem Accurately predicting future power outages to enable rapid recovery.
method Developed a graph conformal prediction method for quarter-hourly outage data.
result Graph conformal prediction method delivers accurate prediction regions for future outage numbers.
Graph neural networks detect structural perturbations from time series data.
problem Detecting structural causes of disturbances in complex systems.
method Graph neural network approach to infer structural perturbations from functional time series.
result Data-driven approach outperforms typical reconstruction methods and meets Bayesian inference accuracy.
Predictive Q-learning algorithm for IoT networks with human operators.
problem Resilient and predictive actions for IoT networks with faulty components.
method Predictive and resilient Q-learning algorithm considering historical data and human operator feedback.
result Optimal scheduling policies avoiding attacked locations and faults.
One of the most defining features of the global financial network is its inherent complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic met…
Paper introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
Graph Attention Networks predict power outage durations from natural disasters.
problem Accurately predicting power outage durations from geospatial and weather data.
method Graph Attention Networks (GAT) for semi-supervised learning.
result GAT model outperforms existing methods by 2% - 15% in accuracy.
Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine t…
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
Propagation of balance-sheet or cash-flow insolvency across financial institutions may be modeled as a cascade process on a network representing their mutual exposures. We derive rigorous asymptotic results for the magnitude of contagion in a large financial network and give an analytical expression for the asymptotic …
Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Adversarial training improves graph autoencoder generalization.
problem Improving graph autoencoder generalization.
method Formulated L2 and L1 adversarial training for graph autoencoders and variational graph autoencoders.
result Adversarial training boosts graph autoencoder and variational graph autoencoder generalization.
Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and distributed learning models to achieve high performance and scalability. However, we find that the feature vectors of benchmark datas…
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
GNNs robustness in community detection is studied with various perturbations.
problem Understanding GNNs robustness in community detection tasks.
method Systematic computational evaluation of six GNN architectures on synthetic and real-world networks.
result Supervised GNNs achieve higher baseline accuracy, while DMoN shows stronger resilience to perturbations.
New model shows negative resilience can improve trading efficiency.
problem Optimal trade execution in limit order books with negative resilience.
method Stochastic order book model with negative resilience.
result Negative resilience can lead to more efficient trading.
Proposes resilience metrics for large blackout costs with logarithmic resilience.
problem Large variations in blackout costs make estimating risk impractical.
method Uses mean of log of large blackout costs, tail slope index, and frequency.
result Solves problems of heavy tail and large variations in blackout costs.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…
ResiliNet improves distributed neural network inference resilience.
problem Physical node failures in distributed neural networks cause performance drops.
method Skip hyperconnection and failout technique.
result ResiliNet provides inference resiliency for distributed neural networks.
New method quantifies resilience of electric distribution systems from historical data.
problem Large blackouts caused by extreme winds have significant costs and impacts.
method Formulate large event risk from utility outage data, quantify resilience improvements through investments.
result Investments in wind hardening and faster restoration can reduce the probability of large cost events.
New framework for resilient bi-criteria optimization under noisy feedback.
problem Bi-criteria combinatorial optimization with noisy function evaluations.
method Introducing (α,β,δ,extttN)-resilience and developing a black-box framework. result Achieves sublinear regret and constraint violation for bi-criteria bandit problems.
New method detects novel node categories in graphs with distribution shifts.
problem Detecting novel node categories in graphs with distribution shifts.
method Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP).
result RECO-SLIP outperforms existing methods in detecting novel node categories.
Paper tackles Byzantine resilience in distributed multi-task learning.
problem Resilience of distributed algorithms in the presence of Byzantine agents.
method Online weight assignment rule based on accumulated loss and filtering.
result Aggregation with proposed weight assignment rule improves expected regret.
This paper uses robust optimization to analyze supply chain resilience.
problem Supply chain resilience analysis of multi-modal logistics networks.
method Robust optimization with budget-of-uncertainty.
result Interactive effects of network size, disruption scale, and degree on resilience.
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries th…
Improved deep neural network generalization through noise resilience.
problem Understanding and predicting generalization error of deep neural networks.
method Noise resilience measures to predict generalization error.
result Secured 5th position in the PGDL competition at NeurIPS 2020.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.
Spectral analysis of neighborhood graphs is one of the most widely used techniques for exploratory data analysis, with applications ranging from machine learning to social sciences. In such applications, it is typical to first encode relationships between the data samples using an appropriate similarity function. Popul…
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
problem Financial resilience of agri-food companies in Spain during the Ukraine-Russia conflict.
method Cluster analysis using centred log-ratios for compositional data of financial ratios.
result Increase in resilient firms by 2023, highlighting sectoral adaptation to economic challenges.
New framework boosts neural network performance and resilience.
problem Susceptibility of compact neural network implementations to system disturbances.
method Realistic crossbar simulations and Mosaics framework to re-use synaptic connections.
result Compact neural networks are noise-immune and perform well under disturbances.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
problem Challenges in learning graph representations due to structure and feature information.
method GIB is an information-theoretic principle that balances expressiveness and robustness by maximizing mutual information between representation and target, while constraining mutual information with input data.
result GIB-based models are more robust to adversarial attacks, achieving up to 31% improvement.
We show that wealth processes in the block-shaped order book model of Obizhaeva/Wang converge to their counterparts in the reduced-form model proposed by Almgren/Chriss, as the resilience of the order book tends to infinity. As an application of this limit theorem, we explain how to reduce portfolio choice in highly-re…
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these …
Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasona…
Study finds ESG investments more resilient than traditional equity indices during market turmoil.
problem Resilience of ESG investments during financial instability.
method Daily returns analysis using MGND and EGARCH-in-mean models.
result ESG investments show higher resilience compared to traditional equity indices during crises.
TensorFI injects faults in TensorFlow programs to assess their reliability.
problem Ensuring reliability of machine learning systems in safety-critical domains.
method TensorFI is a flexible fault injection framework for TensorFlow applications.
result TensorFI evaluates the resilience of 12 ML programs, including autonomous vehicle DNNs.
Ranger improves DNNs' fault resilience without re-computation.
problem Transient faults in DNNs cause errors, reducing reliability.
method Range restriction to transform critical faults to benign faults.
result Significant improvement in error resilience (3x to 50x) with no accuracy loss.
We present a novel method to reconstruct complex network from partial information. We assume to know the links only for a subset of the nodes and to know some non-topological quantity (fitness) characterising every node. The missing links are generated on the basis of the latter quan- tity according to a fitness model …
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
problem Low- to middle-income countries rely heavily on foreign aid for recovery from extreme weather events, which is slow and uncertain.
method Developed a method to form global catastrophe risk pools that maximize risk diversification and select countries with low bilateral correlations or low shares in the pool risk.
result Global pooling increases risk diversification, lowers countries' shares in the pool risk, and increases the number of countries benefiting from risk pooling.
Cryptos remained resilient after SVB's collapse, contrary to expectations.
problem Impact of SVB collapse on crypto markets.
method Factual summary, sentiment analysis, and market performance examination.
result Cryptocurrencies showed resilience after SVB's collapse.