TutorialText can be modeled as graphs — dependency parse graphs, entity co-occurrence networks, document graphs. This tutorial covers text-to-graph conversion, embedding-based node features, and GNN classification over text graphs.
October 04, 2026 · 10 min
TutorialRun TGraphX experiments on Google Colab with one pip install command. This step-by-step tutorial covers graph construction, training, artifact saving, and connecting to Drive for persistence.
October 03, 2026 · 7 min
TutorialGNN explainability methods attribute predictions to input nodes and edges. This tutorial covers gradient saliency, integrated gradients, and edge attribution using TGraphX's explain module.
October 02, 2026 · 10 min
Research NoteSemi-supervised node classification uses a small fraction of labeled nodes to classify the rest. This research note compares GCN, label propagation, and self-training approaches in TGraphX.
October 01, 2026 · 9 min
TutorialIntegrating TGraphX experiments with MLflow for run tracking, metric comparison, and artifact storage — using tgraphx.tracking utilities alongside standard mlflow calls.
September 30, 2026 · 8 min
TutorialGraph classification predicts a label for an entire graph, not individual nodes. This tutorial covers architecture choices (GCN+pool, GIN+pool, SAGPool), GraphBatch for multi-graph training, and evaluation.
September 29, 2026 · 10 min
TutorialA complete end-to-end tutorial for building a multimodal knowledge graph with image and text entities in TGraphX — construction, multimodal encoding, TransE scoring, and link prediction evaluation.
September 28, 2026 · 11 min
TutorialClassical link prediction scoring functions (Common Neighbors, Jaccard, Adamic-Adar, Preferential Attachment) require no training and are essential baselines for any link prediction study.
September 27, 2026 · 9 min
TutorialTGraphX's mining.communities module provides Louvain, label propagation, spectral, and Girvan-Newman community detection with modularity scoring and NMI evaluation.
September 26, 2026 · 8 min
TutorialTGraphX generates self-contained offline HTML reports for GNN experiments — no server, no cloud account needed. This tutorial covers the DashboardReporter API, metric logging, and sharing reports without infrastructure.
September 25, 2026 · 8 min
TutorialDistributed GNN training is harder than it looks. This tutorial covers data parallelism with DDP, graph partitioning challenges, TGraphX's experimental distributed utilities, and honest limitations.
September 24, 2026 · 9 min
ComparisonGraph-level classification requires reducing a set of node representations to a single graph vector. This comparison covers mean, sum, max, and DiffPool hierarchical pooling — what each captures and when each wins.
September 23, 2026 · 9 min
TutorialConfig-driven experiments separate research decisions from implementation details. This tutorial shows how to structure a YAML config for a TGraphX experiment and load it into a reproducible training loop.
September 22, 2026 · 8 min
TutorialCitation networks are a standard GNN benchmark. This end-to-end tutorial loads a Cora-style dataset, constructs a TGraphX Graph, trains a node classifier, and evaluates with proper train/val/test splits.
September 21, 2026 · 8 min
Research NoteThe graph Laplacian's spectrum encodes connectivity, bottlenecks, and community structure. This research note covers spectral graph theory essentials and TGraphX's mining.spectral tools.
September 20, 2026 · 8 min
TutorialTensor node features for large graphs exceed RAM. TGraphX's feature store memory-maps features to disk and fetches only the slices a mini-batch needs, making large-graph training practical.
September 19, 2026 · 8 min
Research NoteAdding layers to a GNN eventually makes all node representations identical — a phenomenon called over-smoothing. This research note explains why it happens, how to measure it, and practical mitigations.
September 18, 2026 · 8 min
ComparisonGCN uses fixed symmetric normalization; GAT learns attention weights. This article explains the practical and theoretical differences, when attention adds value, and the computational cost tradeoff.
September 17, 2026 · 8 min
TutorialTGraphX provides sklearn-style fit/transform/predict estimators for graph learning. This tutorial covers building reproducible pipelines that chain preprocessing, embedding, and classification steps.
September 16, 2026 · 8 min
TutorialLabel propagation spreads supervision signals through a graph without training a neural network. This tutorial covers the algorithm mechanics, convergence, TGraphX implementation, and comparison with GNN semi-supervised approaches.
September 15, 2026 · 8 min
TutorialAnomaly detection on graphs identifies nodes, edges, or subgraphs that deviate from expected structure or behavior. This tutorial covers reconstruction-based and contrastive methods using TGraphX.
September 14, 2026 · 8 min
ComparisonA deep comparison of GraphSAINT subgraph sampling and Cluster-GCN graph partitioning for scalable GNN training — bias-variance tradeoffs, convergence behavior, and TGraphX implementations.
September 13, 2026 · 9 min
TutorialHypergraphs allow edges connecting more than two nodes. This tutorial covers incidence matrices, clique expansion to standard graphs, hyperedge convolution, and TGraphX's experimental hypergraph module.
September 12, 2026 · 8 min
Research NoteGNN classifiers are often miscalibrated — their confidence scores don't reflect true accuracy. This research note covers ECE measurement, temperature scaling, and TGraphX's calibration module.
September 11, 2026 · 8 min
TutorialNode2Vec learns node embeddings via biased random walks and skip-gram training. This tutorial covers the p/q parameters, embedding quality evaluation, downstream classification, and TGraphX's implementation.
September 10, 2026 · 8 min
TutorialGraph autoencoders encode graph structure into latent embeddings and decode them to predict links. This tutorial covers GAE and VGAE architectures, link prediction evaluation, and TGraphX's VGAE implementation.
September 09, 2026 · 8 min
TutorialA tutorial on temporal GNNs with TGraphX: the TGN memory module, TGATConv temporal attention, temporal sampling, and the key design differences between TGN and TGAT.
September 08, 2026 · 10 min
TutorialClassical generators (ER, BA, WS, SBM) extended with tensor-valued features, neural generation as experimental, and TGraphX generation metrics: validity, uniqueness, novelty, and MMD.
September 07, 2026 · 9 min
TutorialA systematic catalog of common GNN errors — NaN loss, shape mismatches, data leakage, poor convergence — and how TGraphX validation and doctor utilities help isolate and fix each one.
September 06, 2026 · 9 min
TutorialA practical deep dive into GraphSAGE with TGraphX: mean, max, and LSTM aggregators, the TensorGraphSAGELayer API, inductive learning on unseen nodes, and when SAGE beats GCN and GAT.
September 05, 2026 · 8 min
TutorialA tutorial for building knowledge graphs with tensor entity features in TGraphX: KG construction, TransE/DistMult/ComplEx/RotatE scoring, multimodal entity features, link prediction evaluation, and KG+RGCN.
September 04, 2026 · 12 min
FAQFrequently asked questions about TGraphX: installation, graph construction, which layer to use, reproducibility, interoperability, and the most common mistakes that lead to silent training failures.
September 03, 2026 · 12 min
TutorialA practical guide to graph RL with TGraphX: two roles graphs play in RL, REINFORCE, DQN, and PPO implementations, supported environments, and honest limitations of the built-in algorithms.
September 02, 2026 · 11 min
TutorialA tutorial on heterogeneous graph learning with TGraphX: HeteroGraph data structure, RGCN for relational graphs, HAN with meta-path attention, HGT for multi-type graphs, and typed neighbor sampling.
September 01, 2026 · 11 min
ArticleScene graphs, brain MRI region networks, superpixel graphs, and video temporal graphs — practical vision use cases where tensor-valued message passing is the natural modeling choice.
August 31, 2026 · 11 min
ArticleA thoughtful look at why small, opinionated graph learning frameworks coexist with PyG and DGL — what large frameworks trade away by optimizing for breadth, and what role specialization plays.
August 30, 2026 · 10 min
Research NoteA 10-point reproducibility checklist for GNN experiments: seeding all RNG sources, seeding the NeighborLoader, fixing splits, recording versions, running multiple seeds, and saving artifacts.
August 29, 2026 · 12 min
ArticleWhere tensor-valued graph learning applies in biology (PPINs, brain connectomes, biological KGs) and materials science (crystal graphs), with honest limitations about what TGraphX does not cover.
August 28, 2026 · 11 min
ComparisonA feature-by-feature comparison of TGraphX and Deep Graph Library (DGL): installation, message passing model, heterogeneous graph support, distributed training, and when to use each.
August 27, 2026 · 11 min
ArticleGraph mining extracts structural properties without labels; GNNs learn from supervision. This article explains when each approach is correct, and how TGraphX combines both in one framework.
August 26, 2026 · 9 min
TutorialA step-by-step beginner quickstart for TGraphX: installation, graph construction, first message-passing layer, reproducible training loop, and easy mode — all under 30 minutes.
August 25, 2026 · 12 min
TutorialEdge features in GNNs can be more than scalars — scene graphs, brain DTI tensors, and molecular bonds carry structured multi-dimensional data. TGraphX preserves this structure through two edge feature modes.
August 24, 2026 · 10 min
ArticleShape mismatches in GNN code produce no error, train to completion, and yield wrong results. This article catalogs the most common silent failure patterns and how TGraphX's validation catches them.
August 23, 2026 · 12 min
ComparisonA balanced technical assessment of PyTorch Geometric: where it excels, where it has real limitations, and what falls outside its scope — without pretending any library is perfect.
August 22, 2026 · 11 min
TutorialA technical guide to Graph Isomorphism Networks: the WL-equivalent aggregation theory, GINEConv with edge features, and TGraphX's TensorGINLayer for spatial node features.
August 21, 2026 · 10 min
ArticleA capstone map of TGraphX as a research platform: the directions it supports today, the explicit boundaries it documents, and what remains on the roadmap.
August 20, 2026 · 10 min
ArticleThe BibTeX, the version, the authorship, and a conservative one-paragraph description — everything you need to cite and describe TGraphX accurately.
August 19, 2026 · 7 min
ArticleA map of the GNN ecosystem and where tensor-native graph learning fits: complementary to PyG, DGL, NetworkX, and PyKEEN, useful when graph elements carry.
August 18, 2026 · 9 min
Research NoteHow TGraphX attributes a tensor GNN's prediction to node pixels and edges: saliency, integrated gradients, gradient and perturbation edge attribution.
August 17, 2026 · 9 min
Research NoteWhen a node is an image patch, a volume block, or a sequence, its natural form is a tensor. How TGraphX builds graphs from these modalities and encodes them.
August 16, 2026 · 9 min
TutorialHow to go from a two-minute TGraphX demo notebook to a reproducible experiment on your own data: install checks, the right starter notebook, and the repro.
August 15, 2026 · 8 min
Research NoteA precise notation guide for tensor-valued graphs: node, edge, message, and graph-level shapes, the rank conventions, and how the notation maps to TGraphX.
August 14, 2026 · 9 min
ArticleWhat turns a research package into a dependable one: TGraphX's regression-test hardening, serialization round-trips, backward-compatible loads.
August 13, 2026 · 9 min
TutorialMake reproducibility the default, not an afterthought: how TGraphX's reproducible() context seeds RNGs, records environment state, and what determinism it.
August 12, 2026 · 8 min
Research NoteFraming graph RL as a Markov decision process whose states are graphs: how TGraphX defines environments, graph-aware policy/value networks, and action.
August 11, 2026 · 10 min
ArticleA breadth-first tour of how you actually drive TGraphX: high-level and explicit Python APIs, CLI tools, the Colab gallery, the local dashboard, and run.
August 10, 2026 · 9 min
Research NoteWhen graph objectives compete, there is no single best graph — there is a Pareto front. How TGraphX implements NSGA-II with non-dominated sorting.
August 09, 2026 · 9 min
TutorialTransductive graph learning has a leakage trap. This guide shows how TGraphX stores masks, checks splits, and guards against train/test contamination.
August 08, 2026 · 9 min
TutorialTGraphX is not an island. Lazy-imported adapters convert PyTorch Geometric, OGB, DGL, and torchvision datasets into tensor-aware Graph objects, with no.
August 07, 2026 · 8 min
Research NoteGraph generation has no single accuracy number. TGraphX scores validity, uniqueness, novelty, diversity, and MMD — each catching a different failure mode.
August 06, 2026 · 9 min
Research NoteHow TGraphX turns a set of tensor-valued nodes into one graph-level representation: permutation-invariant readout, the order of pooling.
August 05, 2026 · 9 min
Research NoteAn L-layer GNN sees an L-hop neighborhood. This note formalizes graph locality, its two scales in tensor GNNs, and where TGraphX exposes k-hop extraction.
August 04, 2026 · 9 min
ArticleA version-by-version look at how TGraphX added LLM-predictable aliases and a public API stability registry without breaking existing code — and why.
August 03, 2026 · 9 min
Research NoteThe math behind TGraphX knowledge-graph models: translation, bilinear, complex, and rotation scoring, plus filtered ranking and tensor-valued entity features.
August 02, 2026 · 10 min
Research NoteThree ways TGraphX makes GNN training fit in memory — neighbor sampling, GraphSAINT subgraphs, and Cluster-GCN partitions — and the bias/variance math.
August 01, 2026 · 10 min
ArticleA breadth-first map of what TGraphX covers across graph-learning tasks, tied to real modules and honest Beta/Experimental/Stable labels rather than marketing.
July 31, 2026 · 10 min
TutorialHow to persist tensor-valued graphs: the lossless .tgx bundle for any-rank tensors, GraphML for interop (with caveats), and the safety notes that matter.
July 30, 2026 · 8 min
TutorialTensor node features are large. The TGraphX feature store keeps them in memory or memory-maps them to disk, fetching only the slices a mini-batch needs.
July 29, 2026 · 8 min
TutorialA practical guide to tracking tensor shapes through a TGraphX model: in_shape/out_shape conventions, what each layer preserves or collapses.
July 28, 2026 · 9 min
Research NoteHow TGraphX computes graph attention when nodes are feature maps: split-head softmax attention, scalar vs channel modes, chunked memory, and what is not.
July 27, 2026 · 10 min
Research NotePermutation equivariance is the defining symmetry of a graph network. Here is the math, and the exact TGraphX test that checks it for tensor layers at 1e-5.
July 26, 2026 · 9 min
Research NoteEdges do not have to be scalar weights. TGraphX lets edge features be vectors or [C, H, W] tensors, and each layer family consumes them with explicit shape.
July 25, 2026 · 9 min
Research NoteA tensor graph lives in two geometries at once: the discrete topology of its edges and the continuous geometry inside each node's tensor. Here is how they.
July 24, 2026 · 9 min
TutorialA developer's tour of the TGraphX Graph object and GraphBatch: fields, validation on construction, disjoint-union batching, the batch vector, and shape rules.
July 23, 2026 · 9 min
Research NoteHow TGraphX decomposes message passing into message, aggregate, and update functions that keep node features as tensors of shape [C, H, W] throughout.
July 22, 2026 · 9 min
Research NoteA research note on graph learning framework content strategy.
July 21, 2026 · 8 min
ComparisonA comparison page on TGraphX knowledge graph toolkit comparison.
July 20, 2026 · 8 min
TutorialA tutorial on interactive graph diagram documentation.
July 19, 2026 · 8 min
PackageA package spotlight on research engineering GNN framework.
July 18, 2026 · 8 min
Research NoteA research note on GNN library source code review.
July 17, 2026 · 8 min
ArticleA blog post on tensor-valued graphs multimodal learning.
July 16, 2026 · 8 min
TutorialA tutorial on graph experiment report template.
July 15, 2026 · 9 min
ComparisonA comparison page on hand rolled graph neural network code.
July 14, 2026 · 8 min
ArticleA blog post on graph learning failure modes.
July 13, 2026 · 8 min
TutorialA tutorial on article to graph NLP pipeline.
July 12, 2026 · 9 min
FAQA faq on TGraphX FAQ PyTorch.
July 11, 2026 · 7 min
Research NoteA research note on reproducibility checklist GNN experiments.
July 10, 2026 · 8 min
ArticleA blog post on graph learning computer vision image patches.
July 09, 2026 · 8 min
TutorialA tutorial on test graph neural network code.
July 08, 2026 · 9 min
ComparisonA comparison page on graph learning tool selection matrix.
July 07, 2026 · 10 min
PackageA package spotlight on TGX artifact graph experiment.
July 06, 2026 · 9 min
ArticleA blog post on GNN shape errors.
July 05, 2026 · 8 min
TutorialA tutorial on GNN library documentation source grounded.
July 04, 2026 · 9 min
Research NoteA research note on research software stability labels.
July 03, 2026 · 8 min
ComparisonA comparison page on graph abstraction PyTorch tensors.
July 02, 2026 · 9 min
TutorialA tutorial on tensor shape diagram GNN.
July 01, 2026 · 8 min
ArticleA blog post on LLM assisted research coding graph API.
June 30, 2026 · 8 min
TutorialA tutorial on profile PyTorch graph neural network workflow.
June 29, 2026 · 9 min
FAQA faq on TGraphX performance FAQ.
June 28, 2026 · 7 min
Research NoteA research note on parallel graph neural network pipeline.
June 27, 2026 · 10 min
TutorialA tutorial on shape checked GNN experiment.
June 26, 2026 · 9 min
ComparisonA comparison page on TGraphX vs DGL.
June 25, 2026 · 7 min
ArticleA blog post on tensor shapes graph neural networks.
June 24, 2026 · 8 min
TutorialA tutorial on tensor-valued graph representation source code.
June 23, 2026 · 9 min
Research NoteA research note on TGraphX performance optimization.
June 22, 2026 · 10 min
Research NoteA practical checklist for making a GNN research codebase open-source-ready. Dependencies, seeds, configs, artifacts, documentation — what reviewers actually need to reproduce your results.
June 21, 2026 · 7 min
ComparisonTreat TGraphX and PyG as cooperating tools rather than competitors. PyG layers, TGraphX containers and tooling — a practical integration recipe.
June 20, 2026 · 5 min
TutorialA systematic GNN debugging workflow using TGraphX's validation utilities, error explainer, and audit tools. Less guessing, more isolation.
June 19, 2026 · 6 min
Research NoteThe .tgx native format preserves tensor-valued node features and experiment metadata that standard graph formats cannot. Why this matters for reproducible GNN research.
June 18, 2026 · 5 min
ArticleHow tensor-valued nodes enable multi-modal graphs where different nodes carry different modalities. With limitations: heterogeneous graphs are Experimental in TGraphX.
June 17, 2026 · 5 min
TutorialTGraphX's graph mining subsystem covers motif counting, centrality, and WL features. A practical walkthrough plus honest comparison to specialized mining tools.
June 16, 2026 · 5 min
ArticleA common GNN experiment in PyTorch is 100+ lines of boilerplate before any research happens. TGraphX's canonical APIs collapse this to one call when you want it, with explicit control when you need it.
June 15, 2026 · 5 min
TutorialA walkthrough of constructing patch graphs from CIFAR-10 images and training a shape-aware GNN that preserves the [3, 8, 8] spatial structure across message passing.
June 14, 2026 · 5 min
ArticleA practical guide to setting up deterministic PyTorch experiments in TGraphX — what seeds to set, what flags to flip, and what the cost is.
June 13, 2026 · 5 min
TutorialA tutorial: model MovieLens as a knowledge graph and train a TransE-based recommendation system in TGraphX.
June 12, 2026 · 6 min
ComparisonNetworkX and TGraphX address different problems. NetworkX is for classical graph algorithms; TGraphX is for graph learning. They complement each other.
June 11, 2026 · 5 min
PackageWhy dataset annotation tools matter for reproducible graph and vision research, and what design principles a good annotation pipeline should have.
June 10, 2026 · 5 min
TutorialSynthetic graph generation in TGraphX — classical generators (ER, BA, WS, SBM) extended with tensor-valued node features, plus an honest assessment of the experimental neural generation module.
June 09, 2026 · 5 min
TutorialA complete tutorial: load MNIST, convert each image to a patch graph, train a tensor-aware node classifier, audit the run.
June 08, 2026 · 5 min
ArticleThe shift from ad-hoc graph research code to explicit APIs with built-in audit artifacts. Why it matters for reproducibility and how it changes day-to-day research practice.
June 07, 2026 · 5 min
TutorialA practical guide to TGraphX's evolutionary optimization subsystem — GA, simulated annealing, and NSGA-II for searching over graph structures.
June 06, 2026 · 4 min
ComparisonA balanced comparison of TGraphX's KG subsystem and PyKEEN, the established library for knowledge graph embedding benchmarks. When each fits.
June 05, 2026 · 5 min
ArticleA look at TGraphX's benchmark artifact system, what gets recorded automatically, and how it supports honest benchmark reporting.
June 04, 2026 · 5 min
TutorialA practical guide to mini-batch graph training in TGraphX using NeighborLoader, GraphSAINT, and ClusterLoader — the three sampling strategies that scale GNNs beyond full-batch training.
June 03, 2026 · 5 min
PackageWhy schema awareness matters in deep learning over structured data, and the design direction that schema-aware architectures take.
June 02, 2026 · 5 min
TutorialA practical introduction to TGraphX's graph reinforcement learning subsystem — environments, algorithms, and what to expect from a research-grade module labeled Experimental.
June 01, 2026 · 5 min
ArticleA decision guide: when flat node feature vectors work well, when they break, and how to tell which category your task falls into.
May 31, 2026 · 5 min
Research NoteGNN benchmark numbers often look more impressive than they are. This note discusses the common evaluation shortcuts that inflate results and how to read benchmark claims more carefully.
May 30, 2026 · 5 min
TutorialA practical tutorial on TGraphX's knowledge graph subsystem: TransE, DistMult, ComplEx, and RotatE, with optional tensor-valued entity features.
May 29, 2026 · 5 min
ArticleAI coding tools produce better graph code when the API is explicit, the error messages are actionable, and the canonical surface is small. TGraphX is designed with this in mind.
May 28, 2026 · 5 min
ArticleMost GNN bugs are shape bugs in disguise. TGraphX provides validation utilities that catch them early, before they show up as obscure runtime errors after hours of training.
May 27, 2026 · 4 min
ComparisonA decision-oriented comparison aimed at researchers choosing a graph learning framework for a specific project. When PyG is right, when TGraphX adds value, and how to test the fit quickly.
May 26, 2026 · 5 min
Research NoteReproducing published GNN results is harder than it should be. This note walks through the hidden sources of non-determinism and how explicit tooling helps.
May 25, 2026 · 5 min
TutorialA step-by-step deeper tutorial: validate a tensor graph, build a custom training loop with NeighborLoader, save artifacts, and reproduce results.
May 24, 2026 · 4 min
ArticleMost GNN frameworks assume every node is a flat vector. That assumption breaks for image patches, volumetric blocks, and sequences. Here is why node feature shape matters and how TGraphX handles it.
May 23, 2026 · 5 min
ArticleTGraphX is a tensor-native graph learning framework for PyTorch. This introduction explains what a TGX graph is, why tensor-valued node features matter, and where the framework fits in the GNN ecosystem.
May 22, 2026 · 7 min
TutorialA step-by-step tutorial for representing graph data with tensor-valued node features in TGraphX — including validation, a first training run, and common mistakes to avoid.
May 22, 2026 · 9 min
ComparisonA balanced comparison of TGraphX and PyTorch Geometric. Both run on PyTorch, but they serve different use cases. This article explains when each framework fits, without pretending one replaces the other.
May 22, 2026 · 10 min