Publications
Graph matching via multi-scale heat diffusion
December 9, 2019
Conference Paper
Published in:
IEEE Intl. Conf. on Big Data, 9-12 December 2019.
Summary
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specifically, we consider a multi-scale heat diffusion on the graphs to create multiple weighted graph representations that incorporate both direct adjacencies as well as local structures induced from the heat diffusion. Then a multi-objective optimization method is used to match vertices across all pairs of graph representations simultaneously. We show that our proposed algorithm performs significantly better than the algorithm that only uses the adjacency matrices, especially when the number of known latent alignments between vertices (seeds) is small. We test the algorithm on a set of graphs and show that at the low seed level, the proposed algorithm performs at least 15–35% better than the traditional graph matching algorithm.
Summary
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specifically, we consider a multi-scale heat diffusion on the graphs to create multiple weighted graph representations that incorporate both direct adjacencies as well as local structures induced...
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Identification and detection of human trafficking using language models
November 26, 2019
Conference Paper
Published in:
European Intelligence and Security Informatics Conf., EISIC, 26-27 November 2019.
Summary
In this paper, we present a novel language model-based method for detecting both human trafficking ads and trafficking indicators. The proposed system leverages language models to learn language structures in adult service ads, automatically select a list of keyword features, and train a machine learning model to detect human trafficking ads. The method is interpretable and adaptable to changing keywords used by traffickers. We apply this method to the Trafficking-10k dataset and show that it achieves better results than the previous models that leverage both ad text and images for detection. Furthermore, we demonstrate that our system can be successfully applied to detect suspected human trafficking organizations and rank these organizations based on their risk scores. This method provides a powerful new capability for law enforcement to rapidly identify ads and organizations that are suspected of human trafficking and allow more proactive policing using data.
Summary
In this paper, we present a novel language model-based method for detecting both human trafficking ads and trafficking indicators. The proposed system leverages language models to learn language structures in adult service ads, automatically select a list of keyword features, and train a machine learning model to detect human trafficking...
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Characterization of disinformation networks using graph embeddings and opinion mining
November 26, 2019
Conference Paper
Published in:
2019 European Intelligence and Security Informatics Conference, EISIC, 26-27 November 2019.
Topic:
Summary
Global social media networks' omnipresent access, real time responsiveness and ability to connect with and influence people have been responsible for these networks' sweeping growth. However, as an unintended consequence, these defining characteristics helped create a powerful new technology for spread of propaganda and false information. We present a novel approach for characterizing disinformation networks on social media and distinguishing between different network roles using graph embeddings and hierarchical clustering. In addition, using topic filtering, we correlate the node characterization results with proxy opinion estimates.We plan to study opinion dynamics using signal processing on graphs approaches using longer-timescale social media datasets with the goal to model and infer influence among users in social media networks.
Summary
Global social media networks' omnipresent access, real time responsiveness and ability to connect with and influence people have been responsible for these networks' sweeping growth. However, as an unintended consequence, these defining characteristics helped create a powerful new technology for spread of propaganda and false information. We present a novel...
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Multi-Objective Graph Matching via Signal Filtering
October 11, 2019
Journal Article
Published in:
IEEE Signal Processing Magazine Special Issue on GSP [submitted]
Topic:
R&D area:
Summary
In this white paper we propose a new method which exploits tools from graph signal processing to solve the graph matching problem, the problem of estimating the correspondence between the vertex sets of two graphs. We recast the graph matching problem as matching multiple similarity matrices where the similarities are computed between filtered signals unique to eachnode. Using appropriate graph filters, these similarity matrices can emphasize long or short range behavior and the method will implicitly search for similarities between the graphs and at multiple scales. Our method shows substantial improvementsover standard methods which use the raw adjacency matrices, especially in low-information environments.
Summary
In this white paper we propose a new method which exploits tools from graph signal processing to solve the graph matching problem, the problem of estimating the correspondence between the vertex sets of two graphs. We recast the graph matching problem as matching multiple similarity matrices where the similarities are...
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Uncovering human trafficking networks through text analysis
October 1, 2019
Conference Paper
Published in:
2019 Grace Hopper Celebration, 1-4 October 2019.
Topic:
R&D area:
Summary
Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex traffickers. This shift has allowed traffickers to list their victims in multiple geographic areas simultaneously, while also improving operational security by using multiple methods of electronic communication with buyers; complicating the ability of law enforcement to disrupt these illicit organizations. In this presentation, we present a novel unsupervised template matching algorithm for analyzing and detecting complex organizations operating on adult service websites. We apply this method to a large corpus of adult service advertisements retrieved from backpage.com, and show that the networks identified through the algorithm match well with surrogate truth data derived from phone number networks in the same corpus. Further exploration of the results show that the proposed method provides deeper insights into the complex structures of sex trafficking organizations, not possible through networks derived from phone numbers alone. This method provides a powerful new capability for law enforcement to more completely identify and gather evidence about trafficking operations
Summary
Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex...
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Detection and characterization of human trafficking networks using unsupervised scalable text template matching
December 10, 2018
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Big Data, 10-13 December 2018, pp. 3111-20.
Summary
Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex traffickers. This shift has allowed traffickers to list their victims in multiple geographic areas simultaneously, while also improving operational security by using multiple methods of electronic communication with buyers; complicating the ability of law enforcement to disrupt these illicit organizations. In this paper, we address this issue and present a novel unsupervised and scalable template matching algorithm for analyzing and detecting complex organizations operating on adult service websites. The algorithm uses only the advertisement content to uncover signature patterns in text that are indicative of organized activities and organizational structure. We apply this method to a large corpus of adult service advertisements retrieved from backpage.com, and show that the networks identified through the algorithm match well with surrogate truth data derived from phone number networks in the same corpus. Further exploration of the results show that the proposed method provides deeper insights into the complex structures of sex trafficking organizations, not possible through networks derived from phone numbers alone. This method provides a powerful new capability for law enforcement to more completely identify and gather evidence about trafficking networks and their operations.
Summary
Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex...
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LLTools: machine learning for human language processing
December 5, 2016
Conference Paper
Published in:
30th Conf. on Neural Info. Processing Syst., NIPS 2016, 5-10 December 2016.
Summary
Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components into larger applications. For the speech processing component, the pySLGR (Speaker, Language, Gender Recognition) tool provides signal processing, standard feature analysis, speech utterance embedding, and machine learning modeling methods in Python. The text processing component in LLTools extracts semantically meaningful insights from unstructured data via entity extraction, topic modeling, and document classification. The entity resolution component in LLTools provides approximate string matching, author recognition and graph-based methods for identifying and linking different instances of the same real-world entity. We show through two applications that LLTools can be used to rapidly create and train research prototypes for human language processing.
Summary
Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components...
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Predicting and analyzing factors in patent litigation
December 5, 2016
Conference Paper
Published in:
30th Conf. on Neural Information Processing System, NIPS 2016, 5-10 December 2016.
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R&D area:
Summary
Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content, and graph features. Metadata features are top-level easily-extractable features, i.e., assignee, number of claims, etc. The content feature performs lexical analysis of the claims associated to a patent. Graph features use relational learning to summarize patent references. We apply our methods on US patents using a labeled data set. Prior work has focused on metadata-only features, but we show that both graph and content features have significant predictive capability. Additionally, fusing all features results in improved performance. We also perform a preliminary examination of some of the qualitative factors that may have significant importance in patent litigation.
Summary
Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content...
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Making #sense of #unstructured text data
December 5, 2016
Conference Paper
Published in:
30th Conf. on Neural Info. Processing Syst., NIPS 2016, 5-10 December 2016.
Topic:
R&D area:
Summary
Automatic extraction of intelligent and useful information from data is one of the main goals in data science. Traditional approaches have focused on learning from structured features, i.e., information in a relational database. However, most of the data encountered in practice are unstructured (i.e., social media posts, forums, emails and web logs); they do not have a predefined schema or format. In this work, we examine unsupervised methods for processing unstructured text data, extracting relevant information, and transforming it into structured information that can then be leveraged in various applications such as graph analysis and matching entities across different platforms. Various efforts have been proposed to develop algorithms for processing unstructured text data. At a top level, text can be either summarized by document level features (i.e., language, topic, genre, etc.) or analyzed at a word or sub-word level. Text analytics can be unsupervised, semi-supervised, or supervised. In this work, we focus on word analysis and unsupervised methods. Unsupervised (or semi-supervised) methods require less human annotation and can easily fulfill the role of automatic analysis. For text analysis, we focus on methods for finding relevant words in the text. Specifically, we look at social media data and attempt to predict hashtags for users' posts. The resulting hashtags can be used for downstream processing such as graph analysis. Automatic hashtag annotation is closely related to automatic tag extraction and keyword extraction. Techniques for hashtags extraction include topic analysis, supervised classifiers, machine translation methods, and collaborative filtering. Methods for keyword extraction include graph-based and topical analysis of text.
Summary
Automatic extraction of intelligent and useful information from data is one of the main goals in data science. Traditional approaches have focused on learning from structured features, i.e., information in a relational database. However, most of the data encountered in practice are unstructured (i.e., social media posts, forums, emails and...
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Matching community structure across online social networks
August 3, 2016
Journal Article
Published in:
arXiv, 3 August 2016.
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R&D area:
Summary
The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecting the community structure. In this paper, we explore several approaches for community detection across online social networks with limited knowledge of username alignment across the networks. We refer to the known alignment of usernames as seeds. We investigate strategies for seed selection and its impact on networks with a different fraction of overlapping vertices. The goal is to study the interplay between network topologies and seed selection strategies, and to understand how it affects the detected community structure. We also propose several measures to assess the performance of community detection and use them to measure the quality of the detected communities in both Twitter-Twitter networks and Twitter-Instagram networks.
Summary
The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across...
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