George Fletcher

Daan de Graaf, Robert Brijder, Soham Chakraborty et al.

The GraphAlg language for graph algorithms enables native support for user-defined graph analytics workloads in databases. In this demonstration, we present a web-based playground for writing and executing GraphAlg programs in the web browser, including an interactive tutorial explaining its key concepts. The playground runs inside the user's web browser without any installation, and is freely available under a permissive license as a reusable library. We present two demonstration scenarios of the publicly available playground website, showing how new users can learn to program in GraphAlg using the tutorial, while expert users can use the playground to prototype and validate their algorithms.

Thomas Mulder, George Fletcher, Nikolay Yakovets

We study the optimization of navigational graph queries, i.e., queries which combine recursive and pattern-matching fragments. Current approaches to their evaluation are not effective in practice. Towards addressing this, we present a number of novel powerful optimization techniques which aim to constrain the intermediate results during query evaluation. We show how these techniques can be planned effectively and executed efficiently towards the first practical evaluation solution for complex navigational queries on real-world workloads. Indeed, our experimental results show several orders of magnitude improvement in query evaluation performance over state-of-the-art techniques on a wide range of queries on diverse datasets.

Pratik Gajane, Akrati Saxena, Maryam Tavakol et al.

Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-supervised learning. However, many dynamic real-world applications can be better modeled using sequential decision-making problems and fair reinforcement learning provides a more suitable alternative for addressing these problems. In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework. We discuss various practical applications in which RL methods have been applied to achieve a fair solution with high accuracy. We further include various facets of the theory of fair reinforcement learning, organizing them into single-agent RL, multi-agent RL, long-term fairness via RL, and offline learning. Moreover, we highlight a few major issues to explore in order to advance the field of fair-RL, namely - i) correcting societal biases, ii) feasibility of group fairness or individual fairness, and iii) explainability in RL. Our work is beneficial for both researchers and practitioners as we discuss articles providing mathematical guarantees as well as articles with empirical studies on real-world problems.

Seiji Maekawa, Yuya Sasaki, George Fletcher et al.

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our framework are two-fold; 1) it transforms various non-scalable GNNs to scale well to large-scale graphs by separating local feature aggregation from weight learning in their graph convolution, 2) it efficiently executes precomputation on GPU for large-scale graphs by decomposing their edges into small disjoint and balanced sets. Through extensive experiments with large-scale graphs, we demonstrate that the transformed GNNs run faster in training time than existing GNNs while achieving competitive accuracy to the state-of-the-art GNNs. Consequently, our transformation framework provides simple and efficient baselines for future research on scalable GNNs.

Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno et al.

Gender imbalance persists across science, technology, engineering, and mathematics (STEM) fields, including computer science, where it appears in researcher demographics, productivity, recognition, hiring, and career progression. Given computer science's rapid expansion and global influence, addressing this imbalance is essential for broadening participation and fueling innovation. Although journal-oriented disciplines exhibit consistent gender imbalances in citation practices, it remains unclear whether similar patterns arise in the conference-centric culture of computer science. Here, we systematically investigate gender imbalance in citations of conference and journal papers in computer science. We find that papers for which a woman is listed as either first or last author receive fewer citations than expected, partly because of homophilic citation tendencies (i.e., authors tend to cite papers that share specific attributes). This imbalance is especially pronounced for conference papers--particularly those published at top-tier venues--relative to journals. Moreover, we find that the prominence of the first or last author and the structure of their local co-authorship networks are potential drivers of these imbalances. By exploring how conference-centric publishing practices can amplify systemic imbalances in computer science, our study offers insights that may inform efforts to foster more equitable representation in academia.

Daniel Aarao Reis Arturi, Christoph Köhnen, George Fletcher et al.

Property graphs often contain tree-shaped substructures, yet they are not captured by existing proposals for graph schemas; likewise, query languages and query engines offer little-to-no native support for managing them systematically. As a first contribution, we report on a micro experiment that demonstrates the optimization potential of treating tree-shaped substructures as first class citizens in graph database systems. In particular, we show that in systems backed by relational engines, we can achieve substantial speedups by leveraging structural indexes, as originally developed for XML databases, to accelerate path queries. Based on our findings, we put forward a vision in which tree-shaped substructures are systematically managed throughout the graph query lifecycle, from modeling and schema design to indexing and query processing, and outline arising research questions.

Tobin South, Subramanya Nagabhushanaradhya, Ayesha Dissanayaka et al. · mit

The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols (like MCP) highlight the demand for clarified best practices in authentication and authorization. Looking ahead, ambitions for highly autonomous agents raise complex long-term questions regarding scalable access control, agent-centric identities, AI workload differentiation, and delegated authority. This OpenID Foundation whitepaper is for stakeholders at the intersection of AI agents and access management. It outlines the resources already available for securing today's agents and presents a strategic agenda to address the foundational authentication, authorization, and identity problems pivotal for tomorrow's widespread autonomous systems.

Viktoriia Makovska, George Fletcher, Julia Stoyanovich

We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION's potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption.

George Fletcher, Olha Nahurna, Matvii Prytula et al.

Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database has an underlying data model without which the system would not be usable. Hence, data models are excellent sites for interrogating the (material, social, political, ...) conditions giving rise to a data system. Towards this, drawing inspiration from literary criticism, we propose to closely read data models in the same spirit as we closely read literary artifacts. Close readings of data models reconnect us with, among other things, the materiality, the genealogies, the techne, the closed nature, and the design of technical systems. While recognizing from literary theory that there is no one correct way to read, it is nonetheless critical to have systematic guidance for those unfamiliar with close readings. This is especially true for those trained in the computing and data sciences, who too often are enculturated to set aside the socio-political aspects of data work. A systematic methodology for reading data models currently does not exist. To fill this gap, we present the CREDAL methodology for close readings of data models. We detail our iterative development process and present results of a qualitative evaluation of CREDAL demonstrating its usability, usefulness, and effectiveness in the critical study of data.

Yuya Sasaki, Sohei Tokuno, Haruka Maeda et al.

Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several questionnaire-based surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were limited in scope, including only a few hundred participants within a single country. In this study, we conduct an international survey to evaluate public perceptions of various fairness metrics in decision-making scenarios. We collected responses from 1,000 participants in each of China, France, Japan, and the United States, amassing a total of 4,000 participants, to analyze the preferences of fairness metrics. Our survey consists of three distinct scenarios paired with four fairness metrics. This investigation explores the relationship between personal attributes and the choice of fairness metrics, uncovering a significant influence of national context on these preferences.

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu et al.

A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult (or impossible) to measure the performance of an agent (i.e. a fitness value) to provide reinforcements since the position of the goal is not known. This requires finding the correct behavior among a vast number of possible behaviors without having the knowledge of the reinforcement signals. In these cases, an exhaustive search may be needed. However, this might not be feasible especially when optimizing artificial neural networks in continuous domains. In this work, we introduce novelty producing synaptic plasticity (NPSP), where we evolve synaptic plasticity rules to produce as many novel behaviors as possible to find the behavior that can solve the problem. We evaluate the NPSP on maze-navigation on deceptive maze environments that require complex actions and the achievement of subgoals to complete. Our results show that the search heuristic used with the proposed NPSP is indeed capable of producing much more novel behaviors in comparison with a random search taken as baseline.

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu et al.

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu et al.

The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and reinforcement signals. However, the distal reward problem arises when the reinforcement signals are not available immediately after each network output to associate the neuron activations that contributed to receiving the reinforcement signal. In this work, we extend Hebbian plasticity rules to allow learning in distal reward cases. We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse to keep track of the activation of the neurons. Delayed reinforcement signals are provided after each episode relative to the networks' performance during the previous episode. We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We compare DSP with an analogous hill climbing algorithm that does not incorporate domain knowledge introduced with the NATs, and show that the synaptic updates performed by the DSP rules demonstrate more effective training performance relative to the HC algorithm.

Yulong Pei, Xin Du, Jianpeng Zhang et al.

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.

Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca et al.

Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architectures tend to be large. Neuroevolution is an effective approach for optimizing ANNs; however, there are two bottlenecks that make their application challenging in case of high-dimensional networks using direct encoding. First, classic evolutionary algorithms tend not to scale well for searching large parameter spaces; second, the network evaluation over a large number of training instances is in general time-consuming. In this work, we propose an approach called the Limited Evaluation Cooperative Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize high-dimensional ANNs. The proposed method aims to optimize the pre-synaptic weights of each post-synaptic neuron in different subpopulations using a Cooperative Co-evolutionary Differential Evolution algorithm, and employs a limited evaluation scheme where fitness evaluation is performed on a relatively small number of training instances based on fitness inheritance. We test LECCDE on three datasets with various sizes, and our results show that cooperative co-evolution significantly improves the test error comparing to standard Differential Evolution, while the limited evaluation scheme facilitates a significant reduction in computing time.