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Found 7 papers, 1 papers with code
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
We ground the practical implications of this work through granular analysis on five real-world datasets with varying global homophily levels, demonstrating that (a) GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, and (b) high local homophily does not necessarily confer high performance for a node.
It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games
Existing efforts to formulate computational definitions of fairness have largely focused on distributional notions of equality, where equality is defined by the resources or decisions given to individuals in the system.
On the Complexity of Learning from Label Proportions
In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given.
Sampling Without Compromising Accuracy in Adaptive Data Analysis
In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms.
A supervised approach to time scale detection in dynamic networks
We introduce a framework that tackles both of these issues: By measuring the performance of the time scale detection algorithm based on how well a given task is accomplished on the resulting network, we are for the first time able to directly compare different time scale detection algorithms to each other.
A Confidence-Based Approach for Balancing Fairness and Accuracy
To help to distinguish between these naive algorithms and more sensible algorithms we propose a new measure of fairness, called resilience to random bias (RRB).
Handling oversampling in dynamic networks using link prediction
We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling.
