24 papers · ranked by Valyu relevance
Yiwei Zhao, Qiushi Lin, Hongbo Kang, Guy E. Blelloch + 5 more
In this paper, we highlight a task-data orchestration abstraction that supports a range of distributed applications, including graph processing and key-value stores. Given a batch of tasks each requesting one or more data items, where both tasks and data are distributed across multiple machines, each task must get…
Davide Rucci, Sebastian Parfeniuc, Matteo Mordacchini, Emanuele Carlini + 2 more
—In this paper, we investigate the parallelization of kcore decomposition, a method used in graph analysis to identify cohesive substructures and assess node centrality. Although efficient sequential algorithms exist for this task, the scale of modern networks requires faster, multicore-ready approaches. To this end…
Yeamin Kaiser, Muhammed Tasnim Bin Anwar, Bholanath Das
Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraphbased methods provide an interpretable bridge between symbolic pattern discovery and continuous embedding learning. Yet…
Seoyong Lee, Jinho Lee
In this paper, we introduce AGIS, an extremely fast AGPM system capable of counting arbitrary patterns from huge graphs. AGIS employs structure-informed neighbor sampling, a novel sampling technique that deviates from uniformness but allocates specific sampling probabilities based on the pattern structure. We first…
Nazanin Yousefian, Kasra Jamshidi, Keval Vora, Anders Miltner
Graph pattern mining is important for analyzing graph data. Graph mining systems typically require answering pattern matching queries, which involve solving the NP-complete subgraph isomorphism problem. To address this, domain experts often develop custom optimization strategies based on exploiting substructural…
Phanindra Reddy Madduru, Bijo Thomas
This paper proposes a preprocessing framework for optimizing large-scale graph database ingestion through intelligent edge filtering based on value ranking. We combine adapted PageRank algorithms with business-specific metrics and edge type importance to evaluate and rank edges, enabling selective retention of…
Heli Sun, Xuechun Liu, Miaomiao Sun, Ruichen Cao + 4 more
The Sparse Subgraph Finding (SGF) problem addresses the challenge of identifying sub-graphs with weak social interactions and sparse connections within a graph, which can be effectively modeled as discovering sparse subsystems in intelligent sensor networks. Traditional methods often rely on manually designed…
Van Ho-Long, Nguyen Ho, Anh-Vu Dinh-Duc, Ha Manh Tran + 4 more
The explosive growth of IoT-enabled sensors is producing enormous amounts of time series data across many domains, offering valuable opportunities to extract insights through temporal pattern mining. Among these patterns, an important class exhibits periodic occurrences, referred to as seasonal temporal patterns…
Shariq Bashir
Mining fault-tolerant (FT) frequent itemsets in noisy datasets is more challenging than conventional frequent itemset mining due to the high cost of evaluating fault-tolerance conditions. Consequently, mining maximal fault-tolerant frequent itemsets (FT-MFIs) is particularly important, as they provide a concise…
Simon Jones, Sabine Hauert
Building a distributed spatial awareness within a swarm of locally sensing and communicating robots enables new swarm algorithms. We use local observations by robots of each other and Gaussian belief propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric…
Samar Samir Khalil, Noha S. Tawfik, Marco Spruit
Federated learning has achieved considerable success for predictive modelling, yet federated descriptive analytics remains largely unexplored. Existing federated pattern mining approaches are predominantly support-based and do not optimise a principled global objective such as Minimum Description Length (MDL). We…
Xiangrui Fan, Yuxuan Yang, Shuo Zhang, Wenlong Cai + 1 more
With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Martin Atzmueller, Carolina Centeio Jorge, Cláudio Rebelo de Sá, Behzad M. Heravi + 2 more
Social interactions are prevalent in our lives. These can be observed, e. g., online using social media, however, also offline specifically using sensors. In such contexts, typically time-stamped interactions are recorded, which can also be inferred from real-time location of humans. Such interaction data can then be…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Muhammad Zeshan Arshad, Ali Algarni
Designing fair and efficient blockchain reward mechanisms requires going beyond raw execution time to account for behavioral variability. We present a simulation framework for evaluating BCRPs using entropy as a systems-level indicator of reward fairness and stability. Three strategies are assessed on simulated miner…
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
Authors not listed
Iron, the most abundant element on Earth by mass (34.6%), primarily exists as iron minerals due to its inherent reactivity. The study of iron mineral phase transformations under changing environmental conditions remains an important research focus due to its geological, environmental, and industrial significance. Yet…
Setareh Rahimi, Stephen Bonner, Avid Afzal, Marta Milo + 2 more
Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise…
Yael Hodaya Moshe, Mini Sharma, Anat Dahan, Hila Gvirts
Despite the growing use of functional near-infrared spectroscopy (fNIRS) hyperscanning to record brain activity simultaneously from interacting individuals in naturalistic settings, most analyses quantify functional connectivity separately for each channel pair. The resulting collection of pairwise estimates is…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
James Read-Tannock, Andrew T. Reid, Etienne Farcot, Martin Schürmann + 1 more
Structural covariance networks (SCNs) represent spatial patterns of covariation in brain morphology, often as a network of connections between nodes representing correlations in grey matter volume or cortical thickness measured by magnetic resonance imaging (MRI). SCNs have been suggested to reveal differences in…
Adithya Gungi, Pradyumna Sepúlveda Delgado, Ines F. Aitsahalia, Marta Blanco-Pozo + 1 more
Flexible, goal-directed behavior depends on learning predictive relationships, yet how reward shapes learned transition structure remains incompletely understood. Here we introduce the Sparse Cognitive Graph, a reinforcement-learning framework in which a continuously updated transition representation is sparsified into…
Matthias Ley, Kinga Kęska-Izworska, Lucas Fillinger, Samuel M Walter + 8 more
Biological network visualization together with graph-based analyses are key techniques in systems biology and network medicine to detect patterns and generate hypotheses regarding disease pathobiology, drug target identification, biomarker prioritization, and digital drug discovery. Network representations provide an…