{"podcast":{"title":"Data Skeptic","slug":"data-skeptic","podcast_index_feed_id":587881,"rss_url":"https://dataskeptic.libsyn.com/rss","website_url":"https://dataskeptic.com","image_url":"https://static.libsyn.com/p/assets/0/e/4/b/0e4bd71bb64c6e45/DS_-_New_Logo_assets_-_JL_DS_Logo_Stacked_-_Color_2.jpg","author":"Kyle Polich","episode_count":601,"summary":"The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/data-skeptic"},"episode":{"title":"Fraud Detection with Graphs","slug":"fraud-detection-with-graphs","published_at":"2025-01-22T03:04:00+00:00","page_url":"https://stenobird.com/podcast/data-skeptic/fraud-detection-with-graphs","show_page_url":"https://stenobird.com/podcast/data-skeptic","url":"https://dataskeptic.com/blog/episodes/2025/fraud-detection-with-graphs","audio_url":"https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/fraud-detection-with-graphs.mp3?dest-id=201630","summary":"In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications. We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets. This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.). Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or having trouble with heterogeneous graphs, his method can tackle them because of the \"locality assumption\" – fraud will be a local phenomenon in the graph – and by relying on this assumption, we can get faster and more accurate results. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com","meta_description":"In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based tech…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2243,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/data-skeptic/episodes/fraud-detection-with-graphs/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/data-skeptic/fraud-detection-with-graphs.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}