tgraphx 1.0.0

Tensor-aware graph neural networks preserving spatial node feature layouts

TGraphX

Preprint: TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning · Sajjadi & Eramian, arXiv 2025

Developed by Arash Sajjadi, PhD Candidate in Computer Science, University of Saskatchewan. Academic supervision: Mark Eramian.

TGraphX is a tensor-native graph intelligence framework for research workflows that combine graph learning, graph mining, knowledge graphs, graph generation, evolutionary optimization, graph reinforcement learning, reproducibility, and dashboard-ready reporting — all in PyTorch, with no mandatory external dependencies.

It preserves multi-dimensional node/edge features ([C, H, W], [C, D, H, W], [D]) through every message-passing step, supports scalable mini-batch samplers (GraphSAINT, Cluster-GCN), multimodal tensor-aware knowledge graphs, 13 graph RL algorithms, classical and neural graph generation, multi-objective evolutionary optimization, a local dashboard with offline HTML export, sklearn-like estimators, and a full benchmark + tutorial suite.

Tensor-native graph learning · mining · generation · optimization · reinforcement learning · dashboard — in one research-focused package.

Graph algorithms · Graph mining · Tensor GNNs · Sampling · FeatureStore · Sparse · Node2Vec/VGAE · Hetero · Temporal · KG · Dashboard · Reproducibility · sklearn API

---

Install

pip install tgraphx

Optional extras (all lazy-imported, none required for the base package):

pip install "tgraphx[tracking]"     # TensorBoard
pip install "tgraphx[mlflow]"       # MLflow
pip install "tgraphx[monitoring]"   # psutil + pynvml (dashboard hardware panel)
pip install "tgraphx[pyg]"          # PyTorch Geometric dataset adapter
pip install "tgraphx[ogb]"          # OGB dataset adapter
pip install "tgraphx[pillow]"       # ImageFolder dataset
pip install "tgraphx[dev]"          # pytest + build + twine

DGL has platform-sensitive wheels and is not packaged as a TGraphX extra. Follow the DGL install guide; the DGL adapter is available either way once DGL is on the import path.

For a custom PyTorch build (CPU-only or specific CUDA), install PyTorch first, then TGraphX:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install tgraphx

---

60-second quickstart

Vector node features:

import torch
from tgraphx import Graph, LinearMessagePassing

x = torch.randn(8, 32)
edge_index = torch.stack([torch.arange(8), (torch.arange(8) + 1) % 8])
g = Graph(x, edge_index)

layer = LinearMessagePassing(in_shape=(32,), out_shape=(64,))
out = layer(g.node_features, g.edge_index)   # [8, 64]
out.sum().backward()

Spatial [C, H, W] node features — preserved through message passing:

import torch
from tgraphx import Graph, ConvMessagePassing

N, C, H, W = 6, 16, 8, 8
g = Graph(torch.randn(N, C, H, W), torch.stack(
    [torch.arange(N), (torch.arange(N) + 1) % N]))

layer = ConvMessagePassing(in_shape=(C, H, W), out_shape=(32, H, W))
out = layer(g.node_features, g.edge_index)   # [6, 32, 8, 8]
out.sum().backward()

A Colab tutorial walks through every workflow:

---

Capability map

Area	Capabilities	Stability	Start here
Tensor-aware graphs	vector / image / volume node features, edge features, graph metadata	Beta	docs/graph_basics.md
Graph algorithms	BFS/DFS, shortest paths, MST, max-flow, matching, coloring	Beta	examples/graph_paths_algorithms_demo.py
Graph mining	motifs, centrality, spectral analysis, WL features, similarity	Beta	docs/graph_mining.md
GNN layers	GCN/SAGE/GAT/GIN/GATv2/APPNP vector layers; tensor GAT/SAGE/GIN/ConvMP	Beta	docs/vector_gnn.md
Sampling & loaders	NeighborLoader, LinkNeighborLoader, GraphLoader, GraphSAINT, Cluster-GCN	Beta	docs/graphsaint.md
Feature store	In-memory & memmap tensor feature storage; NeighborLoader integration	Beta	docs/feature_store.md
Sparse backend	CSR/CSC, coalesce, segment ops, optional torch_scatter acceleration	Beta	docs/backends.md
Knowledge graphs	triples, TransE/DistMult/ComplEx/RotatE, filtered ranking, KG+RGCN, multimodal entity features (image/user/text), temporal KG, reasoning	Beta/Experimental	docs/knowledge_graphs.md
Graph generation	classical generators with tensor features, VGAE generation, autoregressive generation, generation metrics (validity/uniqueness/novelty/diversity/MMD)	Experimental	docs/graph_generation.md
Evolutionary optimization	genetic algorithm, simulated annealing, NSGA-II multi-objective, mutation/crossover/selection	Experimental	docs/evolutionary_graph_optimization.md
Graph reinforcement learning	RL environments (navigation/coloring/max-cut/generation/KG), policy/value/Q networks, REINFORCE/A2C/DQN/PPO	Experimental	docs/graph_reinforcement_learning.md
Hypergraphs	incidence matrix, clique/star expansion	Experimental	examples/graph_algorithms_advanced_demo.py
Heterogeneous graphs	RGCN, HAN, HGT; typed neighbor sampling	Experimental	docs/hetero_gnns.md
Temporal graphs	TGNMemory, TGATConv, time encoding, temporal splits	Experimental	docs/temporal_graph_learning.md
Graph autoencoders	GAE / VGAE; dot-product & MLP edge decoders	Experimental	examples/vgae_link_prediction_demo.py
Representation learning	Node2Vec, DeepWalk, graph embeddings	Beta	examples/node2vec_demo.py
Semi-supervised	label propagation, masks, graph splits	Beta	docs/graph_mining.md
Experiment manager	YAML/JSON configs, runners, callbacks, CLI (tgraphx-train)	Beta	docs/experiments.md
Explainability	saliency, integrated gradients, edge attribution	Beta	docs/explainability.md
sklearn-like API	estimators, GraphPipeline, splits, EarlyStopping	Beta	docs/sklearn_api.md
Calibration	ECE, temperature scaling, reliability diagram data	Beta	tgraphx.calibration
Dashboard	local HTTP server, offline HTML, run artifacts, benchmark panels	Beta	docs/dashboard.md
Reproducibility	set_seed, deterministic mode, reproducibility_report.json	Beta	docs/reproducibility.md
Distributed helpers	rank-zero utilities, DDP wrapping, shard helpers	Experimental	docs/distributed_training.md
OGB / TGB wrappers	optional evaluators with no hidden downloads	Beta (optional)	docs/ogb_tgb_integration.md
Tutorials	CPU-runnable Colab-ready quickstarts for generation, evolutionary optimization, and graph RL	Stable	tutorials/
Benchmarks	13 benchmark scripts with --small --json for CI-friendly validation	Stable	benchmarks/

---

Choose your workflow

Analyse a graph

from tgraphx.mining import graph_summary, degree_statistics

summary = graph_summary(graph.edge_index, num_nodes=graph.num_nodes)
# {'num_nodes': ..., 'num_edges': ..., 'density': ..., 'is_directed': ...}

→ docs/graph_mining.md

Train a GNN

from tgraphx.reproducibility import set_seed
from tgraphx.loaders import NeighborLoader

set_seed(42)
loader = NeighborLoader(graph, fanouts=[15, 10], batch_size=64, seed=42)
for sub, seeds in loader:
    logits = model(sub.node_features, sub.edge_index)

→ docs/vector_gnn.md

Scalable mini-batch training

from tgraphx.graphsaint import GraphSAINTNodeSampler, GraphSAINTLoader

sampler = GraphSAINTNodeSampler(graph, budget=512, num_steps=100, seed=0)
for sub in GraphSAINTLoader(sampler, attach_norm=True):
    out = model(sub.node_features, sub.edge_index)

→ docs/graphsaint.md · docs/cluster_gcn.md

Knowledge graph learning

import torch
from tgraphx.mining import KnowledgeGraph, TransE

heads = torch.tensor([0, 1, 2])
relations = torch.tensor([0, 0, 1])
tails = torch.tensor([1, 2, 0])
kg = KnowledgeGraph(heads, relations, tails, num_entities=3, num_relations=2)
model = TransE(num_entities=kg.num_entities, num_relations=kg.num_relations)

→ examples/knowledge_graph_demo.py

Multimodal tensor-aware knowledge graphs

TGraphX KG can represent different entity types such as image nodes, user nodes, text/document nodes, item nodes, paper nodes, method nodes, and dataset nodes. Each entity type can carry its own tensor features, while relations and triples can also carry features such as weights, timestamps, confidence, or provenance.

image_001 --viewedBy--> user_123
user_123  --wrote-->    text_doc_045
text_doc_045 --describes--> image_001

• Image entities can carry image tensors or precomputed image embeddings through a lightweight modality-specific projector.
• User entities can carry profile vectors or learned user embeddings.
• Text entities should currently be provided as precomputed embeddings (e.g. from a sentence encoder); raw text tokenization is not built in.
• Modality masks handle missing modalities gracefully, so heterogeneous graphs with partially observed features are supported.
• Modality-specific projectors are differentiable: gradients flow through them and the model learns from these features, not only stores them.
• Tested behaviour: image/user/text feature sensitivity is verified, gradients flow through all projectors, and a toy multimodal KG training loop shows loss decrease.

This is not a full vision-language foundation model and makes no claim to SOTA. The image projector is intentionally lightweight. For an end-to-end demo and the API reference:

→ docs/kg_multimodal_tensor_features.md · examples/kg_multimodal_tensor_features_demo.py

Temporal / heterogeneous graphs

from tgraphx.temporal import TGNMemory, TGATConv
from tgraphx.layers.hgt import HGTConv

# TGN memory for temporal link prediction
mem = TGNMemory(num_nodes=N, memory_dim=64, message_dim=64)
mem.update(node_ids, messages, timestamps)  # raises on future-data leakage

→ docs/temporal_graph_learning.md · docs/hetero_gnns.md

Graph generation and evolutionary optimization

from tgraphx.generation import FeatureAwareERGraph, uniqueness_score
from tgraphx.evolutionary import GraphGenome, GeneticAlgorithmOptimizer, GeneticAlgorithmConfig, connectivity_fitness
import torch

# Generate graphs with tensor features
graphs = [FeatureAwareERGraph(n=20, p=0.3, node_feature_dim=8, seed=i) for i in range(10)]
print("Uniqueness:", uniqueness_score(graphs))

# Evolve a graph to maximize connectivity
def make_genome(n=6, seed=0):
    ei = torch.tensor([[0, 1, 2], [1, 2, 0]], dtype=torch.long)
    return GraphGenome(edge_index=ei, num_nodes=n)

config = GeneticAlgorithmConfig(population_size=10, n_generations=20, seed=42)
result = GeneticAlgorithmOptimizer(config, connectivity_fitness).optimize([make_genome(seed=i) for i in range(10)])
print(f"Best connectivity: {result.best_fitness:.4f}")

→ docs/graph_generation.md · docs/evolutionary_graph_optimization.md · examples/classical_graph_generation_demo.py · examples/evolutionary_graph_optimization_demo.py

One-liner graph generation

from tgraphx.generation import run_graph_generation

graphs = run_graph_generation(
    method="barabasi_albert",
    num_graphs=16,
    num_nodes=50,
    m=2,
    node_feature_dim=8,
    seed=42,
)
print(f"Generated {len(graphs.graphs)} graphs. Validity: {graphs.metrics['validity']:.2f}")

→ docs/graph_generation.md · docs/evolutionary_graph_optimization.md

Graph reinforcement learning

from tgraphx.rl import GraphNavigationEnv, GraphEnvConfig, GraphPolicyNetwork, REINFORCEAgent
import torch

ei = torch.tensor([[0, 1, 2, 3], [1, 2, 3, 4]], dtype=torch.long)
env = GraphNavigationEnv(ei, 5, node_features=torch.randn(5, 8), target_node=4,
                         config=GraphEnvConfig(max_steps=20))

policy = GraphPolicyNetwork(node_in_dim=8, hidden_dim=32, num_actions=4)
agent = REINFORCEAgent(policy, torch.optim.Adam(policy.parameters(), lr=1e-3))

for ep in range(50):
    traj = agent.collect_episode(env, max_steps=20)
    agent.update(traj)

→ docs/graph_reinforcement_learning.md · docs/graph_rl_algorithms.md · examples/graph_reinforce_demo.py · examples/graph_dqn_demo.py

One-liner graph RL

from tgraphx.rl import run_graph_rl

result = run_graph_rl(
    env="graph_navigation",
    algorithm="dqn",
    episodes=50,
    seed=42,
)
print(f"Mean return: {result.metrics['mean_return']:.2f}")

→ docs/graph_reinforcement_learning.md

Graph RL algorithms

Algorithm	Action type	Core idea	Stability	One-liner
Random	discrete	Uniform random sampling of valid actions	Beta	run_graph_rl(..., algorithm="random")
Greedy	discrete	Highest Q-value action, no learning	Beta	run_graph_rl(..., algorithm="greedy")
REINFORCE	discrete	Monte Carlo policy gradient + entropy	Experimental	run_graph_rl(..., algorithm="reinforce")
Actor-Critic	discrete	Synchronous actor-critic with GAE	Experimental	run_graph_rl(..., algorithm="actor_critic")
A2C	discrete	Advantage Actor-Critic with GAE	Experimental	run_graph_rl(..., algorithm="a2c")
DQN	discrete	Deep Q-Network + ε-greedy + replay	Experimental	run_graph_rl(..., algorithm="dqn")
Double DQN	discrete	Decoupled action selection/evaluation	Experimental	run_graph_rl(..., algorithm="double_dqn")
Dueling DQN	discrete	V(s) + A(s,a) − mean(A) decomposition	Experimental	run_graph_rl(..., algorithm="dueling_dqn")
PPO	discrete	Clipped surrogate objective	Experimental	run_graph_rl(..., algorithm="ppo")
DDPG	continuous	Deterministic policy + critic, soft update	Experimental	run_graph_rl(..., algorithm="ddpg")
Delayed DDPG	continuous	DDPG with delayed actor updates	Experimental	run_graph_rl(..., algorithm="delayed_ddpg")
TD3	continuous	Twin critics + clipped target noise + delayed update	Experimental	run_graph_rl(..., algorithm="td3")
SAC	continuous	Entropy-regularized twin-critic, stochastic actor	Experimental	run_graph_rl(..., algorithm="sac")

Dashboard-ready experiments

from tgraphx.mining.reports import write_graph_mining_summary
write_graph_mining_summary("logs/graph_mining_summary.json", summary)
# → python -m tgraphx.dashboard logs/

→ docs/dashboard.md

---

Stability labels

Label	Meaning
Beta	Tested, documented; API stable within the v1.x series
Experimental	Correct foundations; API or semantics may evolve in future minor releases
Optional	Requires an optional dependency or explicit --download

---

Why tensor-aware GNNs

Standard GNN frameworks expect flat vector node features. Flattening a [C, H, W] feature map into a [C·H·W] vector discards the spatial structure that makes CNNs effective. TGraphX keeps each node's representation as a tensor and applies 1×1 convolutions during message passing, so every neighbourhood aggregation step acts like a miniature CNN across neighbouring feature maps.

M_{i→j} = φ( X_i, X_j, E_{i→j} )
A_j     = AGG_{i ∈ N(j)} M_{i→j}
X'_j    = ψ( X_j, A_j )

Spatial dimensions H and W are preserved through every learned transform. Aggregations are permutation-invariant; the layers are permutation-equivariant under node relabelling (verified by tests/test_math_invariants_v030.py).

---

Stable core APIs

Component	Class	Input node shape	Notes
Graph data structure	Graph, GraphBatch	any	Validated; supports .to(device)
Tensor-aware GCN-style message passing	ConvMessagePassing	[N, C, H, W]	aggr="sum" / "mean" / "max"; chunked forward
Tensor-aware multi-head GAT	TensorGATLayer	[N, C, H, W]	Softmax over incoming edges per destination per head; two-pass log-sum-exp chunked forward
Tensor-aware GraphSAGE	TensorGraphSAGELayer	[N, C, H, W]	Mean / max aggregation; chunked forward
Tensor-aware GIN / GINEConv	TensorGINLayer	[N, C, H, W]	(1+ε)·h_j + Σ h_i; chunked forward
Vector message passing	LinearMessagePassing	[N, D]	Base layer with linear projections
Custom-layer base class	TensorMessagePassingLayer	any	Override message / update
Vector model-zoo	GCNConv, GATv2Conv, APPNP	[N, D]	New in v0.3.0; permutation-equivariance and tiny-overfit tested
Pooling helpers	global_mean_pool, global_sum_pool, global_max_pool	[N, *]	Per-graph reductions
CNN patch encoder	CNNEncoder	[N, C_in, pH, pW]	Outputs spatial feature maps
Graph classification	GraphClassifier	[N, C, H, W]	Mean / sum / max readout
Node classification	NodeClassifier	[N, D]	Vector features

3-D volumetric node features [N, C, D, H, W] are supported by ConvMessagePassing, TensorGATLayer, TensorGraphSAGELayer, and TensorGINLayer (pass spatial_rank=3).

Factories

from tgraphx import make_layer, build_model

layer = make_layer("gat", in_shape=(8, 4, 4), out_shape=(16, 4, 4), heads=2, residual=True)
model = build_model(
    task="graph_classification", layer="conv",
    in_shape=(3, 4, 4), hidden_shape=(8, 4, 4),
    num_layers=2, num_classes=10, pooling="mean",
)

Task	[N,D]	[N,C,H,W]	[N,C,D,H,W]
node_classification	yes	yes	yes
node_regression	yes	yes	yes
graph_classification	yes	yes	yes
graph_regression	yes	yes	yes
edge_prediction	yes	yes (spatial pool)	yes (spatial pool)

---

Datasets, transforms, metrics, benchmarks

TGraphX ships a unified dataset registry, native synthetic datasets, folder-backed datasets, and optional adapters for torchvision, PyG, DGL, and OGB. TGraphX does not redistribute third-party datasets; adapters delegate download and parsing to the upstream library, and any download requires the user to pass download=True explicitly.

from tgraphx.datasets import list_datasets, dataset_info, get_dataset

print(list_datasets())                        # 34 registered datasets
ds = get_dataset("synthetic:patch_graph", num_graphs=32, seed=0)
g = ds[0]

Cache layout: <root> (or $TGRAPHX_DATA, or ~/.cache/tgraphx/datasets). Inspect with tgraphx.datasets.cache_summary(); clear with clear_cache(...).

Layer	Highlights
tgraphx.datasets	Native synthetic + folder + torchvision/PyG/DGL/OGB adapters; safe atomic downloads with SHA-256 checksums and path-traversal-blocked archive extraction
tgraphx.transforms	Compose, AddSelfLoops, RemoveSelfLoops, ToUndirected, NormalizeFeatures, StandardizeFeatures, AddDegreeFeatures, RandomNodeSplit, RandomLinkSplit, AddDegreeEncoding, AddLaplacianEigenvectors, PatchifyImage, BuildGridGraph, …
tgraphx.metrics	Pure-PyTorch accuracy, top_k_accuracy, precision_recall_f1, classification_report, mae, mse, rmse, r2_score, hits_at_k, mean_reciprocal_rank, ndcg_at_k, roc_auc, average_precision
benchmarks/	benchmark_dataset_loading.py, benchmark_training_synthetic.py, benchmark_tensor_vs_flatten.py, benchmark_transforms.py, benchmark_metrics.py, benchmark_layers.py, benchmark_graph_builders.py, benchmark_sampling.py. Every benchmark accepts --small and --output JSON.

Detailed write-ups: docs/datasets.md, docs/transforms.md, docs/metrics.md, docs/benchmarks.md, docs/dataset_license_policy.md.

Importing tgraphx, tgraphx.datasets, tgraphx.transforms, tgraphx.metrics, tgraphx.experiments, or tgraphx.explain does not import torch_geometric / dgl / ogb.

---

Experiment manager

tgraphx.experiments (new in v0.3.0) is a lightweight, dashboard-compatible experiment manager. YAML / JSON configs are validated against an explicit schema (no eval, no exec). Every run writes its artefacts under run_dir only:

runs/<run_name>/<timestamp>/
├── run_metadata.json           # run name, status, device, seed, version
├── experiment_config.json      # exact config copy
├── experiment_summary.json     # epochs, best metric, final loss
├── metrics.csv                 # dashboard-compatible
└── checkpoints/{best,latest}.pt

from tgraphx.experiments import Runner, load_config

cfg = load_config("examples/configs/synthetic_patch_graph.yaml")
runner = Runner(cfg)
history = runner.fit()

Or via the CLI (registered as console scripts):

tgraphx-train  examples/configs/synthetic_patch_graph.yaml
tgraphx-grid   examples/configs/grid_sweep.yaml
tgraphx-report runs/                  # → Markdown summary

Built-in callbacks: EarlyStopping, ModelCheckpoint, CSVLoggerCallback, LearningRateLogger. Multi-seed and grid sweeps via GridRunner. The dashboard reads the same files when you run tgraphx-dashboard --logdir runs/<run_name>.

See docs/experiments.md for the full schema.

---

Explainability

tgraphx.explain (new in v0.3.0) provides diagnostic explainability tools. They run on CPU by default, do not retain autograd graphs, and never make causal claims about a model's predictions.

from tgraphx.explain import (
    node_feature_saliency, integrated_gradients,
    edge_perturbation_attribution, attention_to_edge_scores,
    patch_saliency_to_image_grid,
    export_explanation_metadata, export_edge_scores_csv,
)

sal      = node_feature_saliency(model, graph, target=label)
ig       = integrated_gradients(model, graph, target=label, steps=16)
edge_imp = edge_perturbation_attribution(model, graph, target=label, max_edges=64)

# For TensorGATLayer outputs.
out, attn = layer(x, edge_index, return_attention=True)
edge_scores = attention_to_edge_scores(attn, edge_index, head_reduce="mean")

# Patch-level heatmap from grid_shape metadata.
heatmap = patch_saliency_to_image_grid(sal, grid_shape=graph.metadata["grid_shape"])

# Export artefacts the dashboard can render (explicit paths only).
export_explanation_metadata("runs/demo/explanation_metadata.json",
                            method="saliency", target=int(label))
export_edge_scores_csv("runs/demo/explanation_edges.csv",
                       graph.edge_index, edge_scores, top_k=20)

See docs/explainability.md for examples and limits.

---

Optional integrations

Adapter	Install	Notes
Native synthetic / folder datasets	(none)	Deterministic, learnable, no network
Torchvision-backed datasets	already a TGraphX base dependency	MNIST, CIFAR-10/100, SVHN, STL-10, FakeData, … converted to patch graphs
PyTorch Geometric	pip install "tgraphx[pyg]"	Planetoid, TUDataset, generic adapter; data-format converters only
DGL	follow upstream install	citation graphs, generic adapter
OGB	pip install "tgraphx[ogb]"	node / link / graph property prediction wrappers + OGBEvaluatorWrapper
MLflow	pip install "tgraphx[mlflow]"	MLflowLogger, lazy import
TensorBoard	pip install "tgraphx[tracking]"	TensorBoardLogger, lazy import
Hardware monitoring	pip install "tgraphx[monitoring]"	psutil + pynvml for the dashboard's hardware panel

TGraphX is interoperable with the PyG / DGL / OGB ecosystems through small data-format converters; it is not a drop-in replacement for either framework.

---

Backend and platform support

Platform	Forward	torch.compile	AMP	CI coverage
Linux + CPU	yes	yes	bfloat16 (recommended)	Full Ubuntu CI on Python 3.10 / 3.11 / 3.12
NVIDIA CUDA	yes	yes	float16 / bfloat16	Local tests; no GPU runners in CI
Apple Silicon MPS	yes	partial	partial	macOS smoke CI (import + build)
Windows + CPU	yes	yes	yes	Windows smoke CI (Python 3.11)
macOS + CPU	yes	yes	yes	macOS smoke CI (Python 3.11)
Multi-GPU (DDP)	helpers	user-managed	user-managed	tgraphx.distributed rank-zero / barrier helpers; full DDP setup is the user's responsibility

For the AMP recommendations, dtype-cast policy, and per-platform caveats, see docs/performance.md. For the precise API stability classification, see docs/api_stability.md.

---

Dashboard, local-first, privacy

TGraphX ships a local-first training dashboard that reads run artefacts (metrics.csv, run_metadata.json, experiment_config.json, experiment_summary.json, dataset_metadata.json, transform_metadata.json, metrics_summary.json, benchmark_results.json, explanation_metadata.json, explanation_edges.csv, explanation_patch_heatmap.json, hetero_graph_metadata.json, temporal_metadata.json, sampling_metadata.json, hardware_report.json).

tgraphx-dashboard --logdir runs/demo
# → http://127.0.0.1:8765 (localhost only, no token needed)

tgraphx-dashboard --logdir runs/demo --host 0.0.0.0 --token MY_SECRET_TOKEN
tgraphx-dashboard --logdir runs/demo --export-html snapshot.html

Properties:

• Off by default. The dashboard is opt-in; nothing about importing TGraphX runs a server.
• Local-first. Reads files from your --logdir; never executes code, never loads checkpoints, never runs models.
• No telemetry, no analytics, no external CDN; the offline HTML export is fully self-contained.
• LAN access requires --token; localhost mode does not.
• Path-traversal protected: every file read is validated against the resolved logdir.
• Background threads are launched only when you call launch_dashboard_background(...) explicitly.

Helpers for writing metadata files the dashboard understands:

from tgraphx import (
    write_run_metadata, write_dataset_metadata, write_transform_metadata,
    write_metrics_summary, write_benchmark_results,
    write_explanation_metadata, write_experiment_config,
    write_hardware_report, write_sampling_metadata,
    write_hetero_graph_metadata, write_temporal_metadata,
)

write_graph_stats from earlier releases is preserved.

See docs/dashboard.md for the full UI, security model, and offline export.

---

Privacy summary

Behaviour	Default
Telemetry / analytics	None — never
Remote calls at import	None
Dashboard	Off — launch explicitly
CSV / TensorBoard / MLflow logging	Off — create a logger explicitly
Hardware monitoring	Off — pass include_hardware=True to env_report
Checkpoints	Off — call save_checkpoint or attach ModelCheckpoint explicitly
Dataset downloads	Off — adapters require download=True
Background threads	None unless launch_dashboard_background is called
File writes	Only to paths the user provides

No ~/.tgraphx directory or user-level config is created.

---

Examples

Graph algorithms and mining

python examples/graph_paths_algorithms_demo.py      # shortest paths, MST, BFS/DFS
python examples/graph_algorithms_advanced_demo.py   # max-flow, matching, coloring
python examples/graph_mining_structural_demo.py     # motifs, centrality, WL
python examples/knowledge_graph_demo.py             # KG triples, TransE
python examples/node2vec_demo.py                    # Node2Vec embeddings

Sampling and scalable training

python examples/neighbor_loader_demo.py             # NeighborLoader / LinkNeighborLoader
python examples/graphsaint_sampler_demo.py          # GraphSAINT node/edge/RW samplers
python examples/cluster_loader_demo.py              # Cluster-GCN partitioners

Neural graph learning

python examples/graph_learning_demo.py              # GCN/SAGE/GAT training
python examples/vgae_link_prediction_demo.py        # GAE / VGAE link prediction
python examples/gat_chunking_demo.py                # chunked GAT forward parity

Graph generation, evolutionary optimization, and RL (Experimental)

python examples/classical_graph_generation_demo.py       # ER/BA/temporal/typed generators
python examples/neural_graph_generation_demo.py          # VGAE/autoregressive/transformer
python examples/evolutionary_graph_optimization_demo.py  # GA, SA, NSGA-II
python examples/graph_rl_environments_demo.py            # all RL environments demo
python examples/graph_reinforce_demo.py                  # REINFORCE on navigation
python examples/graph_dqn_demo.py                        # DQN on graph coloring
python examples/graph_ppo_demo.py                        # PPO on navigation
python examples/graph_td3_sac_demo.py                    # TD3/SAC continuous RL
python examples/generation_rl_high_level_api_demo.py     # one-line API demo
python examples/graph_lstm_sequence_demo.py              # GraphRNN sequence model

Quickstart tutorials (CPU runnable, deterministic)

python tutorials/graph_generation_quickstart.py       # ER/BA/SBM + metrics + dashboard
python tutorials/evolutionary_optimization_quickstart.py # GA/SA/NSGA-II + Pareto front
python tutorials/graph_rl_quickstart.py               # random/DQN/PPO/TD3/SAC comparison

Dashboard, experiments, sklearn API

python examples/training_with_dashboard.py          # fit() + CSVLogger → dashboard
python examples/sklearn_style_graph_pipeline_demo.py # estimator/pipeline API
python examples/distributed_smoke.py --world-size 2 --subprocess-pair  # DDP smoke

All fast examples

python examples/run_all_fast_examples.py            # runs 60+ demos in sequence

Validation scripts

A small set of stand-alone validation scripts proves that the surfaces TGraphX advertises actually work end-to-end:

Script	Purpose
examples/device_validation.py	Runs vector + spatial layer smokes on CPU / CUDA / MPS, optionally under autocast (--amp). Emits a JSON report.
examples/dashboard_artifact_validation.py	Writes every supported dashboard metadata file, exports an offline HTML snapshot, and checks for CDN / token / eval leaks.
examples/experiment_end_to_end_validation.py	Trains a tiny synthetic experiment, asserts dashboard files exist, resumes from a checkpoint.
examples/explainability_end_to_end_validation.py	Trains, runs saliency / integrated gradients / edge perturbation, exports dashboard-readable explanation artefacts.
examples/public_datasets/*.py	Manual, opt-in scripts that exercise the torchvision / PyG / DGL / OGB adapters against real upstream loaders. They require --download, cap dataset size, skip cleanly if the optional package is missing, and never run in CI.

See docs/public_dataset_validation.md and docs/device_validation.md for the exact invocations and policy.

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Maturity and scope

TGraphX is an actively developing pre-1.0 research framework. It already includes tested foundations across tensor-aware GNNs, graph algorithms, graph mining, scalable sampling, sparse utilities, feature stores, dashboard reporting, knowledge graphs, hypergraphs, temporal graphs, heterogeneous GNNs, sklearn-style workflows, calibration, benchmarks, and reproducibility tooling. Newer systems are labeled Beta or Experimental until broader benchmark and real-dataset validation is completed.

Current maturity boundaries:

• Distributed utilities — DDP-aware helpers and a validated two-process CPU/gloo smoke path; broader multi-node training remains a roadmap item.
• GraphSAINT / Cluster-GCN — available as Beta foundations with benchmark scripts; production-scale benchmarks should continue to expand.
• HAN / HGT / TGN / TGAT — Experimental foundations with unit, toy-overfit, and no-leakage validation; broader reference-parity comparisons remain future work.
• OGB / TGB integrations — optional wrappers around official evaluators; require explicit user setup.
• Dense graph operations — some builders (kNN, radius, IoU, fully-connected) are O(N²) and emit warnings on large N; the spectral partitioner is O(N³) and is restricted to ≤ 4096 nodes.
• Per-pixel / per-voxel GAT scores — not shipped; [E, K, H, W] score tensors are memory-prohibitive.

TGraphX is built to complement and interoperate with mature graph ecosystems. PyG and DGL provide large-scale GNN infrastructures; NetworkX provides extensive classical graph algorithms; PyKEEN focuses on knowledge graph embeddings. TGraphX focuses on a different integration point: tensor-aware graph learning, graph mining, reproducible experiments, and dashboard-ready research workflows in one package.

Detailed limitations: docs/limitations.md · Roadmap: docs/roadmap.md

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Citation

@misc{sajjadi2025tgraphxtensorawaregraphneural,
    title={TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning},
    author={Arash Sajjadi and Mark Eramian},
    year={2025},
    eprint={2504.03953},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2504.03953},
}

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Authorship

Software author and maintainer: Arash Sajjadi, PhD Candidate in Computer Science, University of Saskatchewan ([e-mail]). Academic supervision: Mark Eramian, PhD supervisor / academic advisor, University of Saskatchewan.

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License

TGraphX is released under the MIT License.