Human Trafficking
The International Labor Organization estimates that today, more than 24.9 million people around the world are victims of human trafficking.
Credit: kgtoh

How Marinus Analytics uses knowledge graphs powered by Amazon Neptune to combat human trafficking

Traffic Jam leverages machine learning technologies from Amazon Web Services to find patterns in ads posted by sexual traffickers on the internet every day.

The International Labor Organization estimates that today, more than 24.9 million people around the world are victims of human trafficking. Nearly 20% of these victims are sexually exploited.

According to the U.S. State Department 2019 Trafficking in Persons Report, 7,481 traffickers were convicted worldwide in 2018. These numbers may appear low, but they represent an increase of 68% from 2014.

Organizations like Marinus Analytics that leverage the power of machine learning to analyze patterns in the advertisements offering sexual services on the internet are helping increase the number of convictions by providing actionable insights to law enforcement organizations.

Emily Kennedy started working on the idea that would eventually become Marinus Analytics when she was an undergraduate student at Pittsburgh’s Carnegie Mellon University (CMU). Kennedy decided to fight the scourge of human trafficking after a trip to Eastern Europe as a teenager, where she came across orphans believed to be controlled by the Russian mafia begging on the streets.

Marinus Analytics Leaders
Emily Kennedy (l), and Cara Jones are the co-founders of Marinus Analytics. The company focuses on how AI can turn big data online into actionable intelligence.
Credit: Marinus Analytics

Kennedy wanted to leverage the power of big data to help rescue victims of human trafficking. She pitched her idea to researchers at CMU’s machine learning- focused Auton Lab, who were intrigued by Kennedy’s vision. At the Auton Lab, Kennedy connected with researcher and engineer Cara Jones to make the then nascent Traffic Jam product operational.

Traffic Jam leverages machine learning technologies from Amazon Web Services to find patterns in the 300,000 plus ads, many of which are posted by sexual traffickers on the internet every day. Viswanathan’s team at AWS conducted a deep dive exploration of Traffic Jam’s data to arrive at the optimal for storage of crawled ad networks’ data in Amazon Neptune. The team also developed a knowledge graph to capture the information found in online classifieds websites, uncover underlying patterns, surface insights to investigators, and bring criminals to justice.

Today, law enforcement officials use Traffic Jam to find victims of human trafficking and dismantle organized crime rings. In 2019 alone, Traffic Jam was used to identify and rescue an estimated 3,800 victims of sex trafficking.

Small needles in especially large haystacks

Prem Viswanathan is a data scientist with AWS Professional Services, and also an adjunct professor at CMU. At CMU he had met Emily Kennedy, during one of her guest lectures, when she was working on Traffic Jam. Today, at AWS Professional Services, Viswanathan is helping organizations like Marinus Analytics harness the power of machine learning to meet their objectives.

“Identifying an ad posted by an organized crime network is challenging,” Viswanathan says. “First, most of the ads posted on the Internet don’t have structured data. To analyze information effectively, it is necessary to sift through the text of every ad to pull out relevant information like the location, date of posting, images, social media handles and other pertinent information.”

To complicate matters even more, there are millions of ads offering sexual services posted on the internet every day. A majority of these ads are placed by people who are offering these services on their own accord. Traffic Jam is focused on finding victims of human trafficking who are forced into the trade against their will.

Traffic Jam uses knowledge graphs to accomplish this objective. Knowledge graphs comprise entities or nodes. Nodes are distinct entities that hold a piece of information. For example, in Traffic Jam, each ad is represented as a distinct node, as are other criteria such as the ad location, phone number, and the month in which the ad was posted.

Traffic Jam know
Traffic Jam utilizes knowledge graphs to help find human traffickers. The knowledge graph for human trafficking contains more than 1 billion edges connecting ads, phone numbers, images, and other entities.
Credit: Marinus Analytics

Knowledge graphs also store the relationships among these different nodes. They do this in the form of edges. With the rapidly growing number of ads added to the internet every day, the knowledge graph utilized by Traffic Jam contains more than a billion edges connecting ads, phone numbers, images and other entities.

“Traffic Jam sifts through the information contained in these large number of nodes to uncover suspicious patterns,” says Viswanathan. “Consider an example of two ads that have different images, and posted from different locations, but share the same phone number. If you combine text indicators of potential human trafficking to these signals, you arrive at a movement pattern that analysts might identify as problematic, and surface to law enforcement for further review.”

AWS also developed a custom user interface using ReactJS and D3. The user interface enables investigators to visualize the patterns. The knowledge graph-based setup also enables investigators to query up to four times more information than previously feasible, while performing their analysis. This allows them to find prior ads more easily, where a member of a human trafficking network might have used a real phone number or revealed other identifying information.

Deep Graph Learning – an area ripe for innovation

George Karypis is a professor within the Department of Computer Science & Engineering at the University of Minnesota. In the course of his career, Karypis has focused on a variety of areas related to big data including data mining, recommender systems, and high-performance computing. Karypis joined Amazon in 2019 as an Amazon Scholar—a select group of academic professionals that work on large-scale technical challenges while continuing to teach and conduct research at their universities. "The opportunity to help organizations like Marinus Analytics to harness the power of big data, and have a real-world impact is deeply meaningful to me," Karypis said.

George Karypis
Amazon Scholar George Karypis is a professor at the University of Minnesota.

At Amazon, Karypis’ team is focused on unlocking innovations that drive efficient and scalable deep learning on knowledge graphs. The team has been responsible for developing the Deep Graph Library (DGL), an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is a framework that allows developers to program a class of machine learning models called graph neural networks (GNN). DGL supplements existing tensor-based frameworks such as Tensorflow, PyTorch, and MXNet to support the growing area of deep graph learning.

The adoption of GNNs has exploded in recent years, as data scientists move beyond developing deep learning models for 2D signals (such as images) and 3D signals (such as video) to learning from structured, related data embedded in graphs.

Today, GNNs are used in a number of fields. For example, they play an increasingly important role in social networks, where graphs show connections among related people. At Amazon, they are used to develop recommender systems, build mechanisms for fraud and abuse detection and develop Alexa chatbots among other applications.

Organizations like Marinus Analytics use GNNs to contrast information between different nodes, and surface interesting insights, such as whether a particular ad has characteristics common with ads posted by organized crime rings.

For Karypis, GNNs represent one of the most exciting areas in the world of machine learning. More specifically, he believes there are three areas in the world of deep graph learning that are particularly ripe for innovation.

“At the most basic level, there are multiple experiments that are trying to determine the best way to express machine learning models in deep graph learning,” says Karypis. “What are the right models? What are the most appropriate abstractions?”

The integration with Amazon Neptune has been a game changer for Traffic Jam
Cara Jones, CEO, Marinus Analytics

The second challenge pertains to the training of these models. GNN training requires irregular memory accesses. In addition, the training involves fewer operations for each word of memory that it accesses and is computationally demanding. Moreover, knowledge graphs such as the one used by Traffic Jam have billions of data points. “In order to realize the benefits afforded by GNNs, it is critical to develop efficient and scalable distributed GNN training approaches for large graphs,” says Karypis.

Finally, Karypis and his team are intrigued by the most effective ways to compute knowledge graph embeddings. This involves embedding both the entities of a graph and underlying relations in a vector form in a d-dimensional space. For Traffic Jam, representing nodes and their relations in a vector form is what enables the comparison of different ad networks, each of which is represented as a sub-graph.

“Language modelling is a very well understood problem, as are various facets related to computer vision,” he says. “However, it’s still early days when it comes to GNNs, and I’m excited to be at AWS where a lot of the innovation is happening.”

Traffic Jam’s new offerings that use Amazon Neptune and advanced ML techniques to track different ad networks and analyze their likelihood of belonging to an existing crime group is currently in beta. The new features are expected to be made generally available to users soon.

“The integration with Amazon Neptune has been a game changer for Traffic Jam,” says Cara Jones, CEO and co-founder of Marinus Analytics. “Using the knowledge graph and associated sub-graphs, we are now able to capture four times as much information as previously possible. More importantly, we are able to analyze data and identify potential crime groups in real-time, even as new information comes in.”

Research areas

Related content

US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
LU, Luxembourg
Are you a talented and inventive scientist with a strong passion about modern data technologies and interested to improve business processes, extracting value from the data? Would you like to be a part of an organization that is aiming to use self-learning technology to process data in order to support the management of the procurement function? The Global Procurement Technology, as a part of Global Procurement Operations, is seeking a skilled Data Scientist to help build its future data intelligence in business ecosystem, working with large distributed systems of data and providing Machine Learning (ML) and Predictive Modeling expertise. You will be a member of the Data Engineering and ML Team, joining a fast-growing global organization, with a great vision to transform the Procurement field, and become the role model in the market. This team plays a strategic role supporting the core Procurement business domains as well as it is the cornerstone of any transformation and innovation initiative. Our mission is to provide a high-quality data environment to facilitate process optimization and business digitalization, on a global scale. We are supporting business initiatives, including but not limited to, strategic supplier sourcing (e.g. contracting, negotiation, spend analysis, market research, etc.), order management, supplier performance, etc. We are seeking an individual who can thrive in a fast-paced work environment, be collaborative and share knowledge and experience with his colleagues. You are expected to deliver results, but at the same time have fun with your teammates and enjoy working in the company. In Amazon, you will find all the resources required to learn new skills, grow your career, and become a better professional. You will connect with world leaders in your field and you will be tackling Data Science challenges to ensure business continuity, by taking the right decisions for your customers. As a Data Scientist in the team, you will: -be the subject matter expert to support team strategies that will take Global Procurement Operations towards world-class predictive maintenance practices and processes, driving more effective procurement functions, e.g. supplier segmentation, negotiations, shipping supplies volume forecast, spend management, etc. -have strong analytical skills and excel in the design, creation, management, and enterprise use of large data sets, combining raw data from different sources -provide technical expertise to support the development of ML models to facilitate intelligent digital services, such as Contract Lifecycle Management (CLM) and Negotiations platform -cooperate closely with different groups of stakeholders, e.g. data/software engineers, product/program managers, analysts, senior leadership, etc. to evaluate business needs and objectives to set up the best data management environment -create and share with audiences of varying levels technical papers and presentations -deal with ambiguity, prioritizing needs, and delivering results in a dynamic environment Basic qualifications -Master’s Degree in Computer Science/Engineering, Informatics, Mathematics, or a related technical discipline -3+ years of industry experience in data engineering/science, business intelligence or related field -3+ years experience in algorithm design, engineering and implementation for very-large scale applications to solve real problems -Very good knowledge of data modeling and evaluation -Very good understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc. -SQL and query performance tuning skills Preferred qualifications -2+ years of proficiency in using R, Python, Scala, Java or any modern language for data processing and statistical analysis -Experience with various RDBMS, such as PostgreSQL, MS SQL Server, MySQL, etc. -Experience architecting Big Data and ML solutions with AWS products (Redshift, DynamoDB, Lambda, S3, EMR, SageMaker, Lex, Kendra, Forecast etc.) -Experience articulating business questions and using quantitative techniques to arrive at a solution using available data -Experience with agile/scrum methodologies and its benefits of managing projects efficiently and delivering results iteratively -Excellent written and verbal communication skills including data visualization, especially in regards to quantitative topics discussed with non-technical colleagues
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, WA, Seattle
Amazon is seeking an experienced, self-directed data scientist to support the research and analytical needs of Amazon Web Services' Sales teams. This is a unique opportunity to invent new ways of leveraging our large, complex data streams to automate sales efforts and to accelerate our customers' journey to the cloud. This is a high-visibility role with significant impact potential. You, as the right candidate, are adept at executing every stage of the machine learning development life cycle in a business setting; from initial requirements gathering to through final model deployment, including adoption measurement and improvement. You will be working with large volumes of structured and unstructured data spread across multiple databases and can design and implement data pipelines to clean and merge these data for research and modeling. Beyond mathematical understanding, you have a deep intuition for machine learning algorithms that allows you to translate business problems into the right machine learning, data science, and/or statistical solutions. You’re able to pick up and grasp new research and identify applications or extensions within the team. You’re talented at communicating your results clearly to business owners in concise, non-technical language. Key job responsibilities • Work with a team of analytics & insights leads, data scientists and engineers to define business problems. • Research, develop, and deliver machine learning & statistical solutions in close partnership with end users, other science and engineering teams, and business stakeholders. • Use AWS services like SageMaker to deploy scalable ML models in the cloud. • Examples of projects include modeling usage of AWS services to optimize sales planning, recommending sales plays based on historical patterns, and building a sales-facing alert system using anomaly detection.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust Balance Work and Life Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives Mentor & Grow Careers Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables
US, Virtual
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.