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Found 46 papers, 8 papers with code
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text
Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
Leveraging Reinforcement Learning and Large Language Models for Code Optimization
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
Leveraging Graph Diffusion Models for Network Refinement Tasks
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
The evaluation of CompCodeVet on two open-source code datasets shows that CompCodeVet has the ability to improve the training dataset quality for LLMs.
AUTOPARLLM: GNN-Guided Automatic Code Parallelization using Large Language Models
Parallelizing sequentially written programs is a challenging task.
Bias and Fairness in Large Language Models: A Survey
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere.
Fairness-Aware Graph Neural Networks: A Survey
In this article, we examine and categorize fairness techniques for improving the fairness of GNNs.
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis.
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses.
Neural Compositional Rule Learning for Knowledge Graph Reasoning
NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head.
Graph Learning with Localized Neighborhood Fairness
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review
We provide a comprehensive review of the existing literature on memory-augmented GNNs.
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core in heterogeneous hardware platforms.
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach
To account for the structure in the node representations, Mazi generates node representations at each level of the hierarchy, and utilizes them to influence the node representations of the original graph.
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible.
Personalized Visualization Recommendation
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback.
Heterogeneous Graphlets
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neural networks for self-driving vehicle control systems. The rapid increase in the consumption of memory and computational resources by these models demands the use of multi-core parallel systems to scale the execution of the complex emerging applications that depend on them.
Graph Neural Networks with Heterophily
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and biological networks.
Deep Graph Similarity Learning: A Survey
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search.
Temporal Network Sampling
In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted.
NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning
However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions.
Distributed, Parallel, and Cluster Computing Performance Programming Languages
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods.
A View on Deep Reinforcement Learning in System Optimization
We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization.
Adaptive Shrinkage Estimation for Streaming Graphs
We propose a novel adaptive, single-pass sampling framework and unbiased estimators for higher-order network analysis of large streaming networks.
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles.
Dynamic Node Embeddings from Edge Streams
In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks.
Heterogeneous Network Motifs
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis.
Attention Models in Graphs: A Survey
However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.
Predicting Graph Categories from Structural Properties
Even though the current version of this paper is withdrawn, there was no disagreement between authors on the novel work in this paper.
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
HONE: Higher-Order Network Embeddings
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs.
Similarity-based Multi-label Learning
Multi-label classification is an important learning problem with many applications.
Inductive Representation Learning in Large Attributed Graphs
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data.
Network Classification and Categorization
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e. g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs).
Deep Feature Learning for Graphs
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs.
Estimation of Graphlet Statistics
In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e. g., counts) of both connected and disconnected graphlets.
Revisiting Role Discovery in Networks: From Node to Edge Roles
The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.
Relational Similarity Machines
Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks.
Graphlet Decomposition: Framework, Algorithms, and Applications
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level.
A Web-based Interactive Visual Graph Analytics Platform
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks.
Learning the Latent State Space of Time-Varying Graphs
We use the two representations as inputs to a mixture model to learn the latent state transitions that correspond to important changes in the Email graph structure over time.
