{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "graph-neural-networks",
  "label": "Graph Neural Networks",
  "title": "Graph Neural Networks Research",
  "description": "Graph Neural Networks research papers in the Naser Ezzati-Jivan publication catalog.",
  "introduction": "This topic page groups Naser Ezzati-Jivan research papers related to graph neural networks. Each linked record provides the paper's problem, method, findings, limitations, keywords, and authoritative source links.",
  "aliases": [
    "graph neural networks"
  ],
  "search_terms": [
    "Graph Neural Networks"
  ],
  "related_topics": [],
  "canonical_url": "https://threadslab.org/research-publications/topics/graph-neural-networks.html",
  "paper_count": 2,
  "papers": [
    {
      "paper_id": "gnn-link-prediction-microservices",
      "title": "Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures",
      "year": 2025,
      "authors": [
        "Ghazal Khodabandeh",
        "Alireza Ezaz",
        "Majid Babaei",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/gnn-link-prediction-microservices/",
      "canonical_source_url": "https://doi.org/10.1145/3676151.3719362",
      "core_contribution": "The paper applies graph attention networks to predict future interactions in microservice call graphs, supporting proactive monitoring.",
      "tags": [
        "microservices",
        "graph-neural-networks",
        "performance-engineering",
        "predictive-monitoring"
      ],
      "keywords": [
        "microservice call graphs",
        "link prediction",
        "graph attention networks",
        "temporal segmentation",
        "negative sampling",
        "adaptive monitoring"
      ]
    },
    {
      "paper_id": "efficient-unsupervised-latency-culprit-ranking",
      "title": "Efficient Unsupervised Latency Culprit Ranking in Distributed Traces with GNN and Critical Path Analysis",
      "year": 2024,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj",
        "James Miller"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-unsupervised-latency-culprit-ranking/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651841",
      "core_contribution": "The paper combines an unsupervised GraphSAGE model with critical-path-specific latency profiles to detect anomalous requests and rank likely microservice culprits without labelled training data.",
      "tags": [
        "microservices",
        "graph-neural-networks",
        "latency-analysis",
        "root-cause-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "latency culprit ranking",
        "distributed traces",
        "GraphSAGE",
        "graph neural networks",
        "critical path",
        "FIRM dataset",
        "service invocation graph",
        "unsupervised anomaly detection",
        "Top-k ranking"
      ]
    }
  ]
}