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          "article_number": "9147599",
          "title": "Graph Neural Networks for Network Topology Analysis",
          "abstract": "Network topology inference from traffic measurements is a fundamental problem in network management. This paper proposes a graph neural network framework that learns structural representations from sampled flow data to accurately recover physical network topologies under partial observability.",
          "doi": "10.1109/TNET.2020.3001245",
          "authors": {
            "authors": [
              {
                "full_name": "Martinez, Elena",
                "author_order": 1,
                "affiliation": "Universidad Politécnica de Madrid",
                "author_url": "https://ieeexplore.ieee.org/author/38019204"
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              {
                "full_name": "Kurose, James F.",
                "author_order": 2,
                "affiliation": "University of Massachusetts Amherst",
                "author_url": "https://ieeexplore.ieee.org/author/37281948"
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          "publication_title": "IEEE/ACM Transactions on Networking",
          "publication_year": 2020,
          "publication_date": "December 2020",
          "volume": "28",
          "issue": "6",
          "start_page": "2471",
          "end_page": "2484",
          "content_type": "Journals",
          "issn": "1063-6692",
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          "publisher": "IEEE",
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          "author_terms": [
            "graph neural networks",
            "network topology",
            "traffic analysis",
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            "Network topology",
            "Network management",
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