The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring pre-trained models ...

We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE buil...

1 day ago · Add data manually or import it from spreadsheets or CSV files. Visualize and customize your graph colors, labels, titles, legends, layouts, and fonts. Download your generated graph as a ...

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to learn representations of graph-structured data. Two common methods for training GNNs are mini-bat...

Look up Graph, graph, or -graph in Wiktionary, the free dictionary. Wikimedia Commons has media related to Graphs. Graph may refer to: Graph (discrete

Flow graph may refer to: Flow or rooted graph (graph theory), a graph in which a vertex has been distinguished as the root Control-flow graph (computer

A Shared Nearest Neighbor (SNN) graph is a type of graph construction using shared nearest neighbor information, which is a secondary similarity measure based on the rankings induced by a primary $k$-...

Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important ...

A graph associahedron is a polytope dual to a simplicial complex whose elements are induced connected subgraphs called tubes. Graph associahedra generalize permutahedra, associahedra, and cyclohedra, ...

Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track th...

We study the hairy graph homology of a cyclic operad; in particular we show how to assemble corresponding hairy graph cohomology classes to form cocycles for ordinary graph homology, as defined by Kon...

When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when...

Graphs, and sequences of growing graphs, can be used to specify the architecture of mathematical models in many fields including machine learning and computational science. Here we define structured g...

Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which...

Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big grap...

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-re...

Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzi...

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distributed (i.i.d.) assum...

Let $t$ be a positive real number. A graph is called $t$-tough if the removal of any vertex set $S$ that disconnects the graph leaves at most $|S|/t$ components, and all graphs are considered 0-tough....

A recent paper, ``A Graphon-Signal Analysis of Graph Neural Networks'', by Levie, analyzed message passing graph neural networks (MPNNs) by embedding the input space of MPNNs, i.e., attributed graphs ...

Look up Graph, graph, or -graph in Wiktionary, the free dictionary. Wikimedia Commons has media related to Graphs. Graph may refer to: Graph (discrete

Been working on it for a couple of months. the goal is creating a second brain and use it as a resource to find connections and ideas etc....

Practical Gremlin - An Apache TinkerPop Tutorial (⭐ 850)

Points: 2300 | Comments: 847 | Author: km

The \textit{generalized Turán number} $\mathrm{ex}(n, T, F)$ is the maximum possible number of copies of $T$ in an $F$-free graph on $n$ vertices for any two graphs $T$ and $F$. For the book graph $B...

computer science, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context

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A library for creating generic graph data structures and modifying, analyzing, and visualizing them. (⭐ 2140)

Points: 1539 | Comments: 197 | Author: jamesharding

We show that every graph has an induced pseudoforest of at least $n-m/4.5$ vertices, an induced partial 2-tree of at least $n-m/5$ vertices, and an induced planar subgraph of at least $n-m/5.2174$ ver...

In discrete mathematics, particularly in graph theory, a graph is a structure consisting of a set of objects where some pairs of the objects are in some

https://preview.redd.it/ylq3nd51buuf1.png?width=768&format=png&auto=webp&s=7ce9cd25733133311a13ac19f19222bd7c4263d7 I know it is a bit cliche to post your vault, but this is near and dear...

Boost.org graph module (⭐ 368)

The partition of graphs into "nice" subgraphs is a central algorithmic problem with strong ties to matching theory. We study the partitioning of undirected graphs into same-size stars, a problem known...

In mathematics, particularly graph theory, and computer science, a directed acyclic graph (DAG) is a directed graph with no directed cycles. That is, it

...

A modular graph-based Retrieval-Augmented Generation (RAG) system (⭐ 31206)

Carsten Thomassen conjectured that every longest circuit in a 3-connected graph has a chord. We prove the conjecture for graphs having no $K_{3,3}$ minor, and consequently for planar graphs....

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key

We all know that beautiful graph views means nothing. This is just another evidence. This is an graphview of the notes I exported from Capacities (currently I am prefering that app, but I still enjoy ...

Graphs for Everyone (⭐ 15983)

We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities). The problem is formulated as the matching of a graph to a temp...

knowledge graph in Wiktionary, the free dictionary. A knowledge graph is a knowledge base that uses a graph-structured data model. Knowledge Graph may also

im not sure if these are all but this was provided by our school and idk if this might help but ya drive link: [https://drive.google.com/drive/folders/1W6kkSbaTq0DOD6X4lviI7YBFivqBEqyA](https://drive....

Build Real-Time Knowledge Graphs for AI Agents (⭐ 23240)

Many well-known graph drawing techniques, including force directed drawings, spectral graph layouts, multidimensional scaling, and circle packings, have algebraic formulations. However, practical meth...

The Graph is an open-source, decentralized protocol that powers the indexing and querying of blockchain data. It enables developers to build scalable

A week has passed and my graph grew like hell XD So enjoy my current graph!...

Stack trace visualizer (⭐ 19335)

We investigate crossing minimization for 1-page and 2-page book drawings. We show that computing the 1-page crossing number is fixed-parameter tractable with respect to the number of crossings, that t...

knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used

Context: I have OCD and I spend hours a day in obsidian, that's why my vault is huge. Yesterday I was playing in the properties of my graph and love the patterns. Obs.: ITS Theme and I do not uses co...

GPU-accelerated force graph layout and rendering (⭐ 1113)

Let $G$ be a simple connected graph of order $n$. A hamiltonian coloring $c$ of a graph $G$ is an assignment of colors (non-negative integers) to the vertices of $G$ such that $D(u, v)$ + $|c(u) - c(v...

Appendix:Glossary of graph theory in Wiktionary, the free dictionary. This is a glossary of graph theory. Graph theory is the study of graphs, systems of nodes

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Blazing fast, instant realtime GraphQL APIs on all your data with fine grained access control, also trigger webhooks on database events. (⭐ 31908)

The theory of graphons comes with a natural sampling procedure, which results in an inhomogeneous variant of the Erdős--Rényi random graph, called $W$-random graphs. We prove, via the method of mome...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular

No, this isn't a joke. He was actually sent this to redesign for a law firm....

Implementation and experiments of graph embedding algorithms. (⭐ 3839)

We extend the powerful Pullback-Pushout (PBPO) approach for graph rewriting with strong matching. Our approach, called \pbpostrong, exerts more control over the embedding of the pattern in the host gr...