惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Privacy International News Feed
I
Intezer
T
Tenable Blog
S
Schneier on Security
Project Zero
Project Zero
G
GRAHAM CLULEY
酷 壳 – CoolShell
酷 壳 – CoolShell
小众软件
小众软件
Know Your Adversary
Know Your Adversary
博客园 - 司徒正美
The Cloudflare Blog
Recent Commits to openclaw:main
Recent Commits to openclaw:main
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
N
News and Events Feed by Topic
博客园 - 叶小钗
宝玉的分享
宝玉的分享
L
LINUX DO - 热门话题
aimingoo的专栏
aimingoo的专栏
S
Secure Thoughts
Forbes - Security
Forbes - Security
T
The Exploit Database - CXSecurity.com
D
Darknet – Hacking Tools, Hacker News & Cyber Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 【当耐特】
罗磊的独立博客
IT之家
IT之家
H
Hacker News: Front Page
I
InfoQ
云风的 BLOG
云风的 BLOG
S
Security Affairs
M
MIT News - Artificial intelligence
GbyAI
GbyAI
Jina AI
Jina AI
Help Net Security
Help Net Security
Engineering at Meta
Engineering at Meta
大猫的无限游戏
大猫的无限游戏
Webroot Blog
Webroot Blog
L
Lohrmann on Cybersecurity
A
About on SuperTechFans
Attack and Defense Labs
Attack and Defense Labs
The Register - Security
The Register - Security
V
V2EX
G
Google Developers Blog
D
DataBreaches.Net
Apple Machine Learning Research
Apple Machine Learning Research
C
Cybersecurity and Infrastructure Security Agency CISA
J
Java Code Geeks
W
WeLiveSecurity
Cloudbric
Cloudbric
T
Tor Project blog

MarkTechPost

A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks Microsoft Research’s World-R1 Uses Flow-GRPO and 3D-Aware Rewards to Inject Geometric Consistency Into Wan 2.1 Without Architectural Changes A Coding Implementation on Pyright Type Checking Covering Generics, Protocols, Strict Mode, Type Narrowing, and Modern Python Typing IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods Qwen Team Releases FlashQLA: a High-Performance Linear Attention Kernel Library That Achieves Up to 3× Speedup on NVIDIA Hopper GPUs Step by Step Guide to Build a Complete PII Detection and Redaction Pipeline with OpenAI Privacy Filter Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings smol-audio: A Colab-Friendly Notebook Collection for Fine-Tuning Whisper, Parakeet, Voxtral, Granite Speech, and Audio Flamingo 3 A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified How to Build Traceable and Evaluated LLM Workflows Using Promptflow, Prompty, and OpenAI OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters Top 10 Physical AI Models Powering Real-World Robots in 2026 How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control Meet Talkie-1930: A 13B Open-Weight LLM Trained on Pre-1931 English Text for Historical Reasoning and Generalization Research Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering OpenMOSS Releases MOSS-Audio: An Open-Source Foundation Model for Speech, Sound, Music, and Time-Aware Audio Reasoning Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo The LoRA Assumption That Breaks in Production How to Build a Fully Searchable AI Knowledge Base with OpenKB, OpenRouter, and Llama How to Build Smarter Multilingual Text Wrapping with BudouX Through Parsing, HTML Rendering, Model Introspection, and Toy Training Top 7 Benchmarks That Actually Matter for Agentic Reasoning in Large Language Models RAG Without Vectors: How PageIndex Retrieves by Reasoning A Coding Tutorial on Datashader on Rendering Massive Datasets with High-Performance Python Visual Analytics xAI Launches grok-voice-think-fast-1.0: Topping τ-voice Bench at 67.3%, Outperforming Gemini, GPT Realtime, and More A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing Google DeepMind Introduces Vision Banana: An Instruction-Tuned Image Generator That Beats SAM 3 on Segmentation and Depth Anything V3 on Metric Depth Estimation Meet GitNexus: An Open-Source MCP-Native Knowledge Graph Engine That Gives Claude Code and Cursor Full Codebase Structural Awareness A Coding Implementation on Deepgram Python SDK for Transcription, Text-to-Speech, Async Audio Processing, and Text Intelligence A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation DeepSeek AI Releases DeepSeek-V4: Compressed Sparse Attention and Heavily Compressed Attention Enable One-Million-Token Contexts Google DeepMind Introduces Decoupled DiLoCo: An Asynchronous Training Architecture Achieving 88% Goodput Under High Hardware Failure Rates Mend Releases AI Security Governance Framework: Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model OpenAI Releases GPT-5.5, a Fully Retrained Agentic Model That Scores 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures Xiaomi Releases MiMo-V2.5-Pro and MiMo-V2.5: Matching Frontier Model Benchmarks at Significantly Lower Token Cost How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Next Leap to Harness Engineering: JiuwenClaw Pioneers ‘Coordination Engineering’ Photon Releases Spectrum: An Open-Source TypeScript Framework that Deploys AI Agents Directly to iMessage, WhatsApp, and Telegram OpenAI Open-Sources Euphony: A Browser-Based Visualization Tool for Harmony Chat Data and Codex Session Logs Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets Across Specialized AI Domains A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Moonshot AI Releases Kimi K2.6 with Long-Horizon Coding, Agent Swarm Scaling to 300 Sub-Agents and 4,000 Coordinated Steps A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning OpenAI Scales Trusted Access for Cyber Defense With GPT-5.4-Cyber: a Fine-Tuned Model Built for Verified Security Defenders Moonshot AI and Tsinghua Researchers Propose PrfaaS: A Cross-Datacenter KVCache Architecture that Rethinks How LLMs are Served at Scale Meet OpenMythos: An Open-Source PyTorch Reconstruction of Claude Mythos Where 770M Parameters Match a 1.3B Transformer How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost A Coding Implementation to Build an AI-Powered File Type Detection and Security Analysis Pipeline with Magika and OpenAI NVIDIA Releases Ising: the First Open Quantum AI Model Family for Hybrid Quantum-Classical Systems xAI Launches Standalone Grok Speech-to-Text and Text-to-Speech APIs, Targeting Enterprise Voice Developers A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG A Coding Guide for Property-Based Testing Using Hypothesis with Stateful, Differential, and Metamorphic Test Design Anthropic Releases Claude Opus 4.7: A Major Upgrade for Agentic Coding, High-Resolution Vision, and Long-Horizon Autonomous Tasks Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows Top 19 AI Red Teaming Tools (2026): Secure Your ML Models A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities OpenAI Launches GPT-Rosalind: Its First Life Sciences AI Model Built to Accelerate Drug Discovery and Genomics Research Building Transformer-Based NQS for Frustrated Spin Systems with NetKet UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration A Technical Deep Dive into the Essential Stages of Modern Large Language Model Training, Alignment, and Deployment Google AI Launches Gemini 3.1 Flash TTS: A New Benchmark in Expressive and Controllable AI Voice Google DeepMind Releases Gemini Robotics-ER 1.6: Bringing Enhanced Embodied Reasoning and Instrument Reading to Physical AI Google Launches ‘Skills’ in Chrome: Turning Reusable AI Prompts into One-Click Browser Workflows A Coding Implementation of Crawl4AI for Web Crawling, Markdown Generation, JavaScript Execution, and LLM-Based Structured Extraction TinyFish AI Releases Full Web Infrastructure Platform for AI Agents: Search, Fetch, Browser, and Agent Under One API Key NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model A Coding Implementation of MolmoAct for Depth-Aware Spatial Reasoning, Visual Trajectory Tracing, and Robotic Action Prediction MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution How Knowledge Distillation Compresses Ensemble Intelligence into a Single Deployable AI Model Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model Five AI Compute Architectures Every Engineer Should Know: CPUs, GPUs, TPUs, NPUs, and LPUs Compared An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context A Coding Guide to Build Advanced Document Intelligence Pipelines with Google LangExtract, OpenAI Models, Structured Extraction, and Interactive Visualization Google AI Research Introduces PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing A Comprehensive Implementation Guide to ModelScope for Model Search, Inference, Fine-Tuning, Evaluation, and Export
Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations
Sana Hassan · 2026-06-24 · via MarkTechPost

In this tutorial, we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. We start by installing Graphify and supporting graph libraries, then generate a small but connected sample application with configuration, database, authentication, service, API, cache, model, and SQL layers. We extract the graph locally using Graphify’s tree-sitter-based analysis, so we do not need an API key or any LLM backend. After loading the generated graph.json into NetworkX, we analyze the codebase’s structure using file types, relationship types, centrality scores, community detection, and shortest paths among important symbols. Also, we create both static and interactive visualizations, making it easier to understand how modules, classes, functions, and database objects connect across the project.

Installing Graphify and NetworkX

import subprocess, sys
def pip(*pkgs):
   subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False)
pip("graphifyy[sql]", "pyvis", "networkx", "matplotlib")
import os, json, glob, textwrap, warnings
import networkx as nx
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")

We install Graphify along with the graph analysis and visualization libraries needed for the tutorial. We import the required Python modules, including NetworkX for graph processing and Matplotlib for static plotting. We also suppress unnecessary warnings so the notebook output stays clean and focused.

Building the Sample Codebase

ROOT = "sample_app"
os.makedirs(ROOT, exist_ok=True)
FILES = {
"config.py": '''
# Central settings object — used everywhere (expect this to be a "god node").
class Settings:
   def __init__(self):
       self.db_dsn = "postgresql://localhost/app"
       self.jwt_secret = "change-me"
       self.rate_limit = 100
settings = Settings()
''',
"database.py": '''
from config import settings
class DatabasePool:
   """Connection pool. WHY: reuse sockets instead of reconnecting per query."""
   def __init__(self, dsn):
       self.dsn = dsn
       self._conns = []
   def acquire(self):
       return {"dsn": self.dsn}
pool = DatabasePool(settings.db_dsn)
def get_connection():
   return pool.acquire()
''',
"models.py": '''
class User:
   def __init__(self, user_id, email):
       self.user_id = user_id
       self.email = email
class Session:
   def __init__(self, user, token):
       self.user = user
       self.token = token
''',
"cache.py": '''
from config import settings
class RateLimiter:
   # NOTE: naive in-memory limiter; swap for Redis in prod.
   def __init__(self, limit):
       self.limit = limit
       self.hits = {}
   def allow(self, key):
       self.hits[key] = self.hits.get(key, 0) + 1
       return self.hits[key] <= self.limit
limiter = RateLimiter(settings.rate_limit)
''',
"auth.py": '''
from config import settings
from database import get_connection
from models import User, Session
def hash_password(raw):
   return f"hashed::{raw}"
def verify_password(raw, hashed):
   return hash_password(raw) == hashed
class AuthService:
   def __init__(self):
       self.secret = settings.jwt_secret
   def login(self, email, password):
       conn = get_connection()
       user = User(user_id=1, email=email)
       return Session(user=user, token=self.secret + email)
''',
"services.py": '''
from database import get_connection
from models import User
from auth import AuthService
class UserService:
   def __init__(self):
       self.auth = AuthService()
   def register(self, email, password):
       conn = get_connection()
       return User(user_id=2, email=email)
   def authenticate(self, email, password):
       return self.auth.login(email, password)
''',
"api.py": '''
from cache import limiter
from services import UserService
from auth import verify_password
svc = UserService()
def signup_route(email, password):
   if not limiter.allow(email):
       return {"error": "rate limited"}
   return svc.register(email, password)
def login_route(email, password):
   if not limiter.allow(email):
       return {"error": "rate limited"}
   return svc.authenticate(email, password)
''',
"main.py": '''
from api import signup_route, login_route
from database import pool
def run():
   signup_route("[email protected]", "pw")
   return login_route("[email protected]", "pw")
if __name__ == "__main__":
   run()
''',
"schema.sql": '''
CREATE TABLE users (
   user_id  SERIAL PRIMARY KEY,
   email    TEXT UNIQUE NOT NULL
);
CREATE TABLE sessions (
   token    TEXT PRIMARY KEY,
   user_id  INTEGER NOT NULL REFERENCES users(user_id)
);
CREATE VIEW active_sessions AS
SELECT s.token, u.email
FROM sessions s JOIN users u ON s.user_id = u.user_id;
''',
}
for name, body in FILES.items():
   with open(os.path.join(ROOT, name), "w") as f:
       f.write(textwrap.dedent(body).lstrip())
print(f"Wrote {len(FILES)} files to ./{ROOT}/")

We create a realistic sample application with multiple Python modules and one SQL schema file. We design the files to include meaningful cross-module relationships, such as imports, function calls, service dependencies, authentication logic, database access, and rate limiting. We then write all these files to a local sample_app directory, giving Graphify a complete mini-codebase to analyze.

Extracting the Knowledge Graph

res = subprocess.run(
   [sys.executable, "-m", "graphify", "extract", ROOT, "--no-cluster"],
   capture_output=True, text=True
)
print(res.stdout[-1500:] or res.stderr[-1500:])
graph_paths = glob.glob("**/graph.json", recursive=True)
assert graph_paths, "graph.json not found — check the extract output above."
GRAPH_JSON = sorted(graph_paths, key=os.path.getmtime)[-1]
print("Graph file:", GRAPH_JSON)
def load_graphify(path):
   data = json.load(open(path))
   ekey = "links" if "links" in data else ("edges" if "edges" in data else None)
   G = nx.DiGraph() if data.get("directed") else nx.Graph()
   for n in data.get("nodes", []):
       nid = n.get("id")
       G.add_node(nid, **{k: v for k, v in n.items() if k != "id"})
   for e in data.get(ekey or "links", []):
       G.add_edge(e.get("source"), e.get("target"),
                  **{k: v for k, v in e.items() if k not in ("source", "target")})
   G.graph.update(data.get("graph", {}))
   return G
G = load_graphify(GRAPH_JSON)
UG = G.to_undirected()
print(f"\nGraph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
def label(n):
   return G.nodes[n].get("label", str(n))

We run Graphify locally on the generated application and extract the project knowledge graph without using any API key or LLM backend. We locate the generated graph.json file and load it into NetworkX using a version-proof node-link loader. We then convert the graph into an undirected form for easier structural analysis and define a helper function to display readable node labels.

Analyzing Centrality and Communities

from collections import Counter
ftypes  = Counter(d.get("file_type", "?") for _, d in G.nodes(data=True))
rels    = Counter(d.get("relation", "?")  for *_ , d in G.edges(data=True))
conf    = Counter(d.get("confidence", "?") for *_ , d in G.edges(data=True))
print("\nNodes by file_type :", dict(ftypes))
print("Edges by relation  :", dict(rels))
print("Edges by confidence:", dict(conf))
deg = nx.degree_centrality(UG)
btw = nx.betweenness_centrality(UG)
print("\nTop 'god nodes' by degree centrality:")
for n, c in sorted(deg.items(), key=lambda x: -x[1])[:8]:
   print(f"  {label(n):<22} deg={c:.3f}  betweenness={btw.get(n,0):.3f}")
try:
   communities = nx.community.louvain_communities(UG, seed=42)
except Exception:
   communities = list(nx.community.greedy_modularity_communities(UG))
node_comm = {n: i for i, com in enumerate(communities) for n in com}
print(f"\nDetected {len(communities)} communities:")
for i, com in enumerate(communities):
   members = ", ".join(sorted(label(n) for n in com))[:90]
   print(f"  Community {i}: {members}")
def find(substr):
   for n in G.nodes:
       if substr.lower() in label(n).lower():
           return n
   return None
a, b = find("api"), find("DatabasePool")
if a and b and nx.has_path(UG, a, b):
   path = nx.shortest_path(UG, a, b)
   print(f"\nPath {label(a)} -> {label(b)}:")
   print("   " + "  →  ".join(label(p) for p in path))

We analyze the extracted graph by summarizing node types, edge relationships, and confidence levels. We compute degree centrality and betweenness centrality to identify important “god nodes” that connect many parts of the application. We also detect communities in the graph and trace a shortest path between key components to understand how parts of the codebase are connected.

Visualizing the Code Graph

plt.figure(figsize=(13, 9))
pos = nx.spring_layout(UG, k=0.7, seed=42)
nx.draw_networkx_edges(UG, pos, alpha=0.25)
nx.draw_networkx_nodes(
   UG, pos,
   node_color=[node_comm.get(n, 0) for n in UG.nodes],
   node_size=[300 + 4000 * deg.get(n, 0) for n in UG.nodes],
   cmap=plt.cm.tab20, alpha=0.9,
)
top = {n for n, _ in sorted(deg.items(), key=lambda x: -x[1])[:14]}
nx.draw_networkx_labels(UG, pos, {n: label(n) for n in top}, font_size=8)
plt.title("Graphify knowledge graph — size=centrality, color=community")
plt.axis("off"); plt.tight_layout()
plt.savefig("graph_static.png", dpi=130); plt.show()
try:
   from pyvis.network import Network
   net = Network(height="650px", width="100%", bgcolor="#111", font_color="white",
                 notebook=True, cdn_resources="in_line", directed=G.is_directed())
   palette = ["#e6194B","#3cb44b","#4363d8","#f58231","#911eb4",
              "#42d4f4","#f032e6","#bfef45","#fabed4","#469990"]
   for n, d in G.nodes(data=True):
       c = node_comm.get(n, 0)
       net.add_node(n, label=label(n), title=f"{d.get('file_type','?')} · {d.get('source_file','')}",
                    color=palette[c % len(palette)], size=12 + 60 * deg.get(n, 0))
   for s, t, d in G.edges(data=True):
       net.add_edge(s, t, title=d.get("relation", ""))
   net.save_graph("graph_interactive.html")
   print("\nSaved interactive graph -> graph_interactive.html")
   from IPython.display import HTML, display
   display(HTML(open("graph_interactive.html").read()))
except Exception as e:
   print("Interactive viz skipped:", e)
for cmd in (
   ["query", "what connects auth to the database?", "--graph", GRAPH_JSON],
   ["path",  "AuthService", "DatabasePool", "--graph", GRAPH_JSON],
   ["explain", "RateLimiter", "--graph", GRAPH_JSON],
):
   print("\n$ graphify " + " ".join(cmd))
   r = subprocess.run([sys.executable, "-m", "graphify", *cmd],
                      capture_output=True, text=True)
   print((r.stdout or r.stderr)[:1200])
print("\nDone. Artifacts: graph_static.png, graph_interactive.html,",
     "and graphify-out/ (graph.json, GRAPH_REPORT.md).")

We visualize the knowledge graph using both static and interactive methods. We first create a Matplotlib graph where node size represents centrality and node color represents community membership. We then build an interactive Pyvis visualization and run Graphify’s CLI commands to query the graph, find paths, and explain selected symbols.

Conclusion

In conclusion, we have a complete local pipeline for converting source code into a useful knowledge graph and studying it with graph analytics. We saw how Graphify extracts meaningful relationships from a Python and SQL codebase, and we use NetworkX to identify central “god nodes,” detect communities, and trace paths between components such as authentication and database logic. We also generated visual outputs that help us inspect the architecture from both a high-level and interactive perspective. This workflow gives us a pathway to reason about code structure, dependency flow, architectural hotspots, and cross-file connections without relying on external APIs, making it useful for codebase exploration, documentation, refactoring, and software architecture analysis.


Check out the Full Codes hereAlso, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us

Sana Hassan

Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.