The 19th International Workshop on Mining and Learning with Graphs (MLG) is held in Long Beach, CA, USA and is co-located with the Tenth International Workshop on Deep Learning on Graphs (DLG) as part of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. In doing so, we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs. Topics of interest include, but are not limited to, graph mining, graph machine learning, statistical relational learning, social network analysis, and network science. The target audience spans researchers and practitioners across academia, government, and industry.

The 17th International Workshop on Mining and Learning with Graphs (MLG) is held in Washington DC, USA on August 15, 2022 and is co-located with the Eighth International Workshop on Deep Learning on Graphs (DLG) as part of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. In doing so, we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs. Topics of interest include, but are not limited to, graph mining, statistical relational learning, social network analysis, and network science. The target audience spans researchers and practitioners across academia, government, and industry.

Scientific software has become a key asset to reproduce and understand the products of scientific research in many disciplines. However, scientific software is becoming increasingly complex and, as a result, researchers need to spend a significant amount of time finding, reading and understanding software documentation to set it up. In this paper we describe SoMEF, a Software Metadata Extraction Framework designed to help highlighting the most important parts of scientific software documentation. SoMEF processes the README files in GitHub repositories to automatically extract which parts of their text refer to the description, installation, invocation, or citation of a software component. Despite its simple features, SoMEF successfully categorizes README excerpts with a minimum 0.92 precision and 0.90 ROC AUC. These results, tested on a corpus of over 70 scientific software repositories, are a promising start towards automatically generating knowledge graphs of scientific software metadata.

The first International Workshop on Heterogeneous Networks Analysis and Mining is held in Los Angeles, California, USA on February 9th, 2018 and is co-located with the 11th ACM International Conference on Web Search and Data Mining. The goal of this workshop is to bring together computing researchers and practitioners to address challenges in the mining and analysis of real-world heterogeneous networks. This workshop has an exciting program that spans a number of subareas including: graph mining, learning from structured data, statistical relational learning, and network science in general. The program includes six invited speakers, lively discussion on emerging topics, and presentations of several original research papers.

MOTIVATION: As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions (DDIs) can guide in vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their associated targets, such methods must effectively fuse multiple sources of information and perform inference over the network of drugs. RESULTS: We propose a probabilistic approach for jointly inferring unknown DDIs from a network of multiple drug-based similarities and known interactions. We use the highly scalable and easily extensible probabilistic programming framework Probabilistic Soft Logic We compare against two methods including a state-of-the-art DDI prediction system across three experiments and show best performing improvements of more than 50% in AUPR over both baselines. We find five novel interactions validated by external sources among the top-ranked predictions of our model. AVAILABILITY AND IMPLEMENTATION: Final versions of all datasets and implementations will be made publicly available. CONTACT: [email protected].

Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in evolving multi-relational social networks. We model a social network as a time-stamped multi-relational graph where vertices represent users, and edges represent different activities between them. To identify spammer accounts, our approach makes use of structural features, sequence modelling, and collective reasoning. We leverage relational sequence information using k-gram features and probabilistic modelling with a mixture of Markov models. Furthermore, in order to perform collective reasoning and improve the predictive power of a noisy abuse reporting system, we develop a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable. We use Graphlab Create and Probabilistic Soft Logic (PSL) to prototype and experimentally evaluate our solutions on internet-scale data from Tagged.com. Our experiments demonstrate the effectiveness of our approach, and show that models which incorporate the multi-relational nature of the social network significantly gain predictive performance over those that do not.

As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender system. Our hybrid approach, HyPER (HYbrid Probabilistic Extensible Recommender), incorporates and reasons over a wide range of information sources. Such sources include multiple user-user and item-item similarity measures, content, and social information. HyPER automatically learns to balance these different information signals when making predictions. We build our system using a powerful and intuitive probabilistic programming language called probabilistic soft logic, which enables efficient and accurate prediction by formulating our custom recommender systems with a scalable class of graphical models known as hinge-loss Markov random fields. We experimentally evaluate our approach on two popular recommendation datasets, showing that HyPER can effectively combine multiple information types for improved performance, and can significantly outperform existing state-of-the-art approaches.

Drug-target interaction studies are important because they can predict drugs' unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL). Using probabilistic rules in PSL, we predict interactions with models based on triad and tetrad structures. We apply (blocking) techniques that make link prediction in PSL more efficient for drug-target interaction prediction. We then perform extensive experimental studies to highlight different aspects of the model and the domain, first comparing the models with different structures and then measuring the effect of the proposed blocking on the prediction performance and efficiency. We demonstrate the importance of rule weight learning in the proposed PSL model and then show that PSL can effectively make use of a variety of similarity measures. We perform an experiment to validate the importance of collective inference and using multiple similarity measures for accurate predictions in contrast to non-collective and single similarity assumptions. Finally, we illustrate that our PSL model achieves state-of-the-art performance with simple, interpretable rules and evaluate our novel predictions using online data sets.

We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.