{"id":50,"date":"2024-11-19T08:47:06","date_gmt":"2024-11-19T08:47:06","guid":{"rendered":"https:\/\/live.21lab.co\/hanotek\/?p=50"},"modified":"2024-11-19T08:49:54","modified_gmt":"2024-11-19T08:49:54","slug":"build-trustworthy-graph-neural-networks","status":"publish","type":"post","link":"https:\/\/live.21lab.co\/hanotek\/build-trustworthy-graph-neural-networks\/","title":{"rendered":"Build trustworthy graph neural networks"},"content":{"rendered":"\n<p class=\"colorDark has-medium-font-size\">Artificial intelligence (AI) is rapidly transforming our world, from recommending products to diagnosing diseases. Indeed,\u00a0Graph Neural Networks<strong>\u00a0<\/strong>(GNNs)<strong>,<\/strong>\u00a0a type of AI that excels at analyzing complex relationships within graph data, can be used for a number of applications such as:<\/p>\n\n\n\n<ul class=\"wp-block-list list-style1\">\n<li><strong>Recommending products or content<\/strong>\u202fon social media and e-commerce platforms.<\/li>\n\n\n\n<li><strong>Detecting fraud and anomalies<\/strong>\u202fin financial transactions and network activity.<\/li>\n\n\n\n<li><strong>Discovering new drugs and materials<\/strong>\u202fin scientific research.<\/li>\n\n\n\n<li><strong>Analyzing social networks and interactions<\/strong>\u202fto understand human behavior.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-drop-cap\">Given that AI and GNNs are increasingly being used in ways that can have a significant social impact and can influence an individual\u2019s life chances, such as in medicine, it\u2019s crucial to ensure these systems are\u202ftrustworthy. In this blog post, we give an overview of how the power of GNNs is being harnessed, how they can be implemented, and best practices to increase the trustworthiness of GNNs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What are Graph Neural Networks?<\/h2>\n\n\n\n<p>Graphs (or networks) are data structures that are present in different contexts of biology, engineering, physics, and economics that are composed from two fundamental elements: nodes and edges. Here, nodes represent entities, and the edges represent the relationships between those entities. Using this combination of nodes and edges, GNNs can be used to represent multiple types of data, including:<\/p>\n\n\n\n<ul class=\"wp-block-list list-style1\">\n<li><strong>Technological networks:<\/strong>\u202finternet, telephone network, power grids, transportation networks, delivery networks<\/li>\n\n\n\n<li><strong>Networks of information:<\/strong>\u202fthe World Wide Web, citation networks, peer-to-peer networks, recommendation networks<\/li>\n\n\n\n<li><strong>Social networks:<\/strong>\u202fonline social networks (Facebook, Instagram, Bluesky), offline social networks (networks formed during a pandemic, friends, work)<\/li>\n\n\n\n<li><strong>Biological networks:<\/strong>\u202fbiochemical networks, brain networks, ecological networks<\/li>\n<\/ul>\n\n\n\n<p>We can illustrate a transportation network with the London underground map, where stations represent the nodes, and the tube lines between stations represent the edges. From the GNN, optimization algorithms can be applied in this context to evaluate, for example, which is the better route for travel from a given station A to station B.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are GNNs used for?<\/h3>\n\n\n\n<p>Graph Neural Networks (GNNs) have emerged as powerful tools in various real-world applications, and, &nbsp;as research and development in trustworthy GNNs progresses, we can expect even wider adoption and impact across diverse fields.<\/p>\n\n\n\n<p>Examples of their applications include social recommendation, where platforms like Pinterest leverage GNNs like\u202fPinSage\u202fto personalize content suggestions based on user interests and social connections. They can also be used for\u00a0traffic prediction\u202fby modelling traffic flow within road networks, enabling optimized traffic management strategies.<\/p>\n\n\n\n<p>GNNs also play a crucial role in\u202ffraud detection\u202fby analyzing financial transaction graphs to identify anomalous patterns. This can help to facilitate early interventions where fraud is detected and prevent individuals becoming victim to financial crime, and even prevent fraud in the first place. Elsewhere, the pharmaceutical industry utilizes GNNs for\u202fdrug discovery, where they analyze molecular structures to predict the properties of potential drug candidates.<\/p>\n\n\n\n<p>Furthermore, GNNs have applications in\u202fnatural language processing, where they analyze relationships between words and sentences to enhance tasks such as sentiment analysis and machine translation. These applications, in turn, have a wide range of uses, from\u00a0marketing\u00a0to\u00a0immigration.<\/p>\n\n\n\n<p>These diverse applications demonstrate the versatility and effectiveness of GNNs in solving complex problems across various domains.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How are Graph Neural Networks implemented?<\/h3>\n\n\n\n<p>Implementing a GNN involves several steps. Broadly, they take the form of:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list list-style1\">\n<li><strong>Data Preparation:<\/strong>\u202fRepresent your data as a graph with nodes and edges, including relevant features for both.<\/li>\n\n\n\n<li><strong>Model Selection:<\/strong>\u202fChoose a suitable GNN architecture based on your task and data characteristics. Popular architectures include Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Recurrent Networks (GRNs).<\/li>\n\n\n\n<li><strong>Model Training:<\/strong>\u202fTrain the GNN model on your prepared graph data, optimizing its parameters to minimize prediction errors.<\/li>\n\n\n\n<li><strong>Evaluation and Inference:<\/strong>\u202fEvaluate the trained model\u2019s performance on a separate test dataset and then use it to make predictions on new, unseen data.<\/li>\n<\/ol>\n\n\n\n<p>Several popular frameworks and libraries facilitate GNN implementation, including:<\/p>\n\n\n\n<ul class=\"wp-block-list list-style1\">\n<li><strong>PyTorch Geometric (PyG):<\/strong>\u202fA widely used Python library offering various GNN architectures, data loaders, and utilities.<\/li>\n\n\n\n<li><strong>Deep Graph Library (DGL):<\/strong>\u202fAnother popular Python library providing efficient implementations of GNNs and supporting diverse graph tasks.<\/li>\n\n\n\n<li><strong>TensorFlow GNN:<\/strong>\u202fA TensorFlow-based library offering GNN components and functionalities for building and training GNN models.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">The current limitations of GNNs<\/h3>\n\n\n\n<p>While GNNs offer great potential, they also raise concerns about potential harms like:<\/p>\n\n\n\n<ul class=\"wp-block-list list-style1\">\n<li><strong>Vulnerability to adversarial attacks:<\/strong>\u202fMalicious actors can manipulate graph data to fool GNNs, leading to incorrect or harmful predictions.<\/li>\n\n\n\n<li><strong>Bias or discrimination:<\/strong>\u202fGNNs can perpetuate\u00a0biases\u00a0hidden within data, leading to unfair outcomes for certain groups or individuals.<\/li>\n\n\n\n<li><strong>Privacy breaches:<\/strong>\u202fSensitive information\u00a0within graph data can be leaked or inferred from GNN models.<\/li>\n\n\n\n<li><strong>Excessive resource consumption:<\/strong>\u202fTraining and deploying GNNs can be computationally expensive and energy-intensive, impacting the environment.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">How can GNNs be made more trustworthy?<\/h2>\n\n\n\n<p>Researchers are now actively exploring ways to build trustworthy GNNs to address these concerns, focusing on six key aspects:<\/p>\n\n\n\n<ul class=\"wp-block-list list-style1\">\n<li>Robustness:\u202fMaking GNNs resistant to adversarial attacks and random errors.<\/li>\n\n\n\n<li>Explainability<strong>:<\/strong>\u202fDeveloping methods to understand and explain GNN predictions, making them more transparent.<\/li>\n\n\n\n<li>Privacy:\u202fImplementing techniques to protect confidential data within GNN models and graph data.<\/li>\n\n\n\n<li>Fairness<strong>:<\/strong>\u202fEnsuring GNNs treat individuals and groups fairly, regardless of sensitive attributes like race or gender.<\/li>\n\n\n\n<li>Accountability<strong>:<\/strong>\u202fEstablishing clear lines of responsibility for GNN system behavior and providing mechanisms to detect and address violations.<\/li>\n\n\n\n<li><strong>Environmental Well-being:<\/strong>\u202fImproving the efficiency of GNNs to reduce their resource consumption and environmental impact.<\/li>\n<\/ul>\n\n\n\n<p>Building trustworthy GNNs requires a shift in focus from solely pursuing high performance to prioritizing these trust-oriented characteristics. This involves developing new methods and adapting existing techniques to the unique challenges of graph data.<\/p>\n\n\n\n<p>Furthermore, it\u2019s crucial to recognize the\u202f<strong>interplay between different aspects of trustworthiness<\/strong>. For example, improving\u00a0explainability\u00a0can help identify vulnerabilities and design more robust GNNs. Similarly, techniques for ensuring fairness can also enhance privacy by reducing the risk of sensitive information leakage.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Take steps to increase trust in AI<\/h2>\n\n\n\n<p>Research on trustworthy GNNs is still in its early stages, but it\u2019s a rapidly growing field with significant potential. By developing GNNs that are not only accurate but also robust, explainable, privacy-preserving, fair, accountable, and environmentally friendly, we can ensure that these powerful AI systems are used responsibly and ethically for the benefit of society.<\/p>\n\n\n\n<p>Schedule a demo\u00a0to find out how Holistic AI can help you increase the trust in your AI.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence (AI) is rapidly transforming our world, from recommending products to diagnosing diseases. Indeed,\u00a0Graph Neural Networks\u00a0(GNNs),\u00a0a type of AI that excels at analyzing complex relationships within graph data, can be used for a number of applications such as: Given that AI and GNNs are increasingly being used in ways that can have a significant [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":44,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2,18],"tags":[12,16,17],"class_list":["post-50","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-and-ai","category-data-science","tag-data","tag-networks","tag-neural"],"acf":[],"_links":{"self":[{"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/posts\/50","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/comments?post=50"}],"version-history":[{"count":1,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/posts\/50\/revisions"}],"predecessor-version":[{"id":51,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/posts\/50\/revisions\/51"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/media\/44"}],"wp:attachment":[{"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/media?parent=50"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/categories?post=50"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/live.21lab.co\/hanotek\/wp-json\/wp\/v2\/tags?post=50"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}