OpenRaiser/Intern-Atlas huggingface dataset:
Under the Hood of Social Media Advertising: How Do We use AI Responsibly for Advertising Targeting and Creative Evaluation
Authors: Aleksandr Farseev
Venue: WSDM
Year: 2023

Digital Advertising is historically one of the most developed areas where Machine Learning and AI have been applied since its origination. From smart bidding to creative content generation and DCO, AI is well-demanded in the modern digital marketing industry and partially serves as a backbone of most of the state-of-the-art computational advertising systems, making them impossible for the AI tech and the programmatic systems to exist apart from one another. At the same time, given the drastic growth of the available AI technology nowadays, the issue of responsible AI utilization as well as the balance between the opportunity of deploying AI systems and the possible borderline etic and privacy-related consequences are still yet to be discussed comprehensively in both business and research communities. Particularly, an important issue of automatic User Profiling use in modern Programmatic systems like Meta Ads as well as the need for responsible application of the creative assessment models to fit into the business etic guidelines is yet to be described well. Therefore, in this talk, we are going to discuss the technology behind modern programmatic bidding and content scoring systems and the responsible application of AI by SoMin.ai to manage the Advertising targeting and Creative Validation process.
Efficient Graph Learning for Anomaly Detection Systems
Authors: Falih Gozi Febrinanto
Venue: WSDM
Year: 2023

Anomaly detection plays a significant role in preventing from detrimental effects of abnormalities. It brings many benefits in real-world sectors ranging from transportation, finance to cybersecurity. In reality, millions of data do not stand independently, but they might be connected to each other and form graph or network data. A more advanced technique, named graph anomaly detection, is required to model that data type. The current works of graph anomaly detection have achieved state-of-the-art performance compared to regular anomaly detection. However, most models ignore the efficiency aspect, leading to several problems like technical bottlenecks. This project mainly focuses on improving the efficiency aspect of graph anomaly detection while maintaining its performance.
Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering
Authors: Chakib Fettal, Lazhar Labiod, Mohamed Nadif
Venue: WSDM
Year: 2023

Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph applications. Furthermore, many of them can handle only specific types of multi-view graphs. Another limitation is that the process of learning graph representations is separated from the clustering process, and in some cases these methods do not even learn a graph representation, which severely restricts their flexibility and usefulness. In this paper we propose a simple yet effective linear model that addresses the dual tasks of multi-view attributed graph representation learning and clustering in a unified framework. The model starts by performing a first-order neighborhood smoothing step for the different individual views, then gives each one a weight corresponding to its importance. Finally, an iterative process of simultaneous clustering and representation learning is performed w.r.t. the importance of each view, yielding a consensus embedding and partition of the graph. Our model is generic and can deal with any type of multi-view graph. Finally, we show through extensive experimentation that this simple model consistently achieves competitive performances w.r.t. state-of-the-art multi-view attributed graph clustering models, while at the same time having training times that are shorter, in some cases by orders of magnitude.
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Authors: Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang
Venue: WSDM
Year: 2023

Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes --- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes. Furthermore, due to various time factors and the annotation preferences of human experts, the heterophily and homophily can change across training and testing data, which is called structural distribution shift (SDS) in this paper. The mainstream methods are built on graph neural networks (GNNs), benefiting the classification of normals from aggregating homophilous neighbors, yet ignoring the SDS issue for anomalies and suffering from poor generalization. This work solves the problem from a feature view. We observe that the degree of SDS varies between anomalies and normal nodes. Hence to address the issue, the key lies in resisting high heterophily for anomalies meanwhile benefiting the learning of normals from homophily. Since different labels correspond to the difference of critical anomaly features which make great contributions to the GAD, we tease out the anomaly features on which we constrain to mitigate the effect of heterophilous neighbors and make them invariant. However, the prior distribution of anomaly features is dynamic and hard to estimate, we thus devise a prototype vector to infer and update this distribution during training. For normal nodes, we constrain the remaining features to preserve the connectivity of nodes and reinforce the influence of the homophilous neighborhood. We term our proposed framework asGraph Decomposition Network (GDN). Extensive experiments are conducted on two benchmark datasets, and the proposed framework achieves a remarkable performance boost in GAD, especially in an SDS environment where anomalies have largely different structural distribution across training and testing environments. Codes are open-sourced in https://github.com/blacksingular/wsdm_GDN.
FineSum: Target-Oriented, Fine-Grained Opinion Summarization
Authors: Suyu Ge, Jiaxin Huang, Yu Meng, Jiawei Han
Venue: WSDM
Year: 2023

Target-oriented opinion summarization is to profile a target by extracting user opinions from multiple related documents. Instead of simply mining opinion ratings on a target (e.g., a restaurant) or on multiple aspects (e.g., food, service) of a target, it is desirable to go deeper, to mine opinion on fine-grained sub-aspects (e.g., fish). However, it is expensive to obtain high-quality annotations at such fine-grained scale. This leads to our proposal of a new framework, FineSum, which advances the frontier of opinion analysis in three aspects: (1) minimal supervision, where no document-summary pairs are provided, only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to a specific subject or characteristic within each general aspect; and (3) phrase-based summarization, where short phrases are taken as basic units for summarization, and semantically coherent phrases are gathered to improve the consistency and comprehensiveness of summary. Given a large corpus with no annotation, FineSum first automatically identifies potential spans of opinion phrases, and further reduces the noise in identification results using aspect and sentiment classifiers. It then constructs multiple fine-grained opinion clusters under each aspect and sentiment. Each cluster expresses uniform opinions towards certain sub-aspects (e.g., "fish" in "food" aspect) or characteristics (e.g., "Mexican" in "food" aspect). To accomplish this, we train a spherical word embedding space to explicitly represent different aspects and sentiments. We then distill the knowledge from embedding to a contextualized phrase classifier, and perform clustering using the contextualized opinion-aware phrase embedding. Both automatic evaluations on the benchmark and quantitative human evaluation validate the effectiveness of our approach.
CoQEx: Entity Counts Explained
Authors: Shrestha Ghosh, Simon Razniewski, Gerhard Weikum
Venue: WSDM
Year: 2023

For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg.de/.
Telecommunication Traffic Forecasting via Multi-task Learning
Authors: Xiaochuan Gou, Xiangliang Zhang
Venue: WSDM
Year: 2023

Accurate telecommunication time series forecasting is critical for smart management systems of cellular networks, and has a special challenge in predicting different types of time series simultaneously at one base station (BS), e.g., the SMS, Calls, and Internet. Unlike the well-studied single target forecasting problem for one BS, this distributed multi-target forecasting problem should take advantage of both the intra-BS dependence of different types of time series at the same BS and the inter-BS dependence of time series at different BS. To this end, we first propose a model to learn the inter-BS dependence by aggregating the multi-view dependence, e.g., from the viewpoint of SMS, Calls, and Internet. To incorporate the interBS dependence in time series forecasting, we then propose a Graph Gate LSTM (GGLSTM) model that includes a graph-based gate mechanism to unite those base stations with a strong dependence on learning a collaboratively strengthened prediction model. We also extract the intra-BS dependence by an attention network and use it in the final prediction. Our proposed approach is evaluated on two real-world datasets. Experiment results demonstrate the effectiveness of our model in predicting multiple types of telecom traffic at the distributed base stations.
Student Behavior Pattern Mining and Analysis: Towards Smart Campuses
Authors: Teng Guo, Feng Xia
Venue: WSDM
Year: 2023

Understanding student behavior patterns is fundamental to building smart campuses. However, the diversity of student behavior and the complexity of educational data not only bring great obstacles to the relevant research, but also leads to unstable performance and low reliability of current student behavior analysis systems. The emergence of educational big data and the latest advances in deep learning and representation learning provide unprecedented opportunities to tackle the above problems. In this talk, we introduce how we mine and analyze student behavior patterns by overcoming the complexity of educational data. Specifically, we propose a series of algorithmic frameworks, which take advantage of network science, data mining, and machine learning to form a data-driven system for mining and analyzing student behavior patterns. Our research not only fills the gap in the field of student abnormal behavior warning and student status monitoring, but also provides insights into data-driven smart city construction.
Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion
Authors: Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patrícia G. C. Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev
Venue: WSDM
Year: 2023

Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming---siloed in so called "echo chambers" of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics---daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.
From Classic GNNs to Hyper-GNNs for Detecting Camouflaged Malicious Actors
Authors: Venus Haghighi
Venue: WSDM
Year: 2023

Graph neural networks (GNNs), which extend deep learning models to graph-structured data, have achieved great success in many applications such as detecting malicious activities. However, GNN-based models are vulnerable to camouflage behavior of malicious actors, i.e., the performance of existing GNN-based models has been hindered significantly. In this research proposal, we follow two research directions to address this challenge. One direction focuses on enhancing the existing GNN-based models and enabling them to identify both camouflaged and non-camouflaged malicious actors. In this regard, we propose to explore an adaptive aggregation strategy, which empowers GNN-based models to handle camouflage behavior of fraudsters. The other research direction concentrates on leveraging hypergraph neural networks (hyper-GNNs) to learn nodes' representation for more effective identification of camouflaged malicious actors.
Simplifying Graph-based Collaborative Filtering for Recommendation
Authors: Li He, Xianzhi Wang, Dingxian Wang, Haoyuan Zou, Hongzhi Yin, Guandong Xu
Venue: WSDM
Year: 2023

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets.
Responsible AI for Trusted AI-powered Enterprise Platforms
Authors: Steven C. H. Hoi
Venue: WSDM
Year: 2023

With the rapidly growing AI market opportunities and the accelerated adoption of AI technologies for a wide range of real-world applications, responsible AI has attracted increasing attention in both academia and industries. In this talk, I will focus on the topics of responsible AI in the industry settings towards building trusted AI-powered enterprise platforms. I will share our efforts and experience of responsible AI for enterprise at Salesforce, from defining the principles to putting them into practice to build trust in AI. Finally, I will also address some emerging challenges and open issues of recent generative AI advances and call for actions of joint responsible AI efforts from academia, industries and governments.
Global Counterfactual Explainer for Graph Neural Networks
Authors: Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, Ambuj K. Singh
Venue: WSDM
Year: 2023

Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.
SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta
Authors: Alfred Huang, Qi Yang, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, Yu-Yi Chu-Farseeva, Aleksandr Farseev
Venue: WSDM
Year: 2023

In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.
Self-supervised Multi-view Disentanglement for Expansion of Visual Collections
Authors: Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni
Venue: WSDM
Year: 2023

Image search engines enable the retrieval of images relevant to a query image. In this work, we consider the setting where a query for similar images is derived from a collection of images. For visual search, the similarity measurements may be made along multiple axes, or views, such as style and color. We assume access to a set of feature extractors, each of which computes representations for a specific view. Our objective is to design a retrieval algorithm that effectively combines similarities computed over representations from multiple views. To this end, we propose a self-supervised learning method for extracting disentangled view-specific representations for images such that the inter-view overlap is minimized. We show how this allows us to compute the intent of a collection as a distribution over views. Finally, we show how effective retrieval can be performed by prioritizing candidate expansion images that match the intent of a query collection.
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Authors: Cheng Ji, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Philip S. Yu
Venue: WSDM
Year: 2023

Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. However, the incremental learning issue of CL has rarely been studied, which brings the limitation in applying it to real-world applications. Contrastive learning identifies the samples with the negative ones from the noise distribution that changes in the incremental scenarios. Therefore, only fitting the change of data without noise distribution causes bias, and directly retraining results in low efficiency. To bridge this research gap, we propose a self-supervised Incremental Contrastive Learning (ICL) framework consisting of (i) a novel Incremental InfoNCE (NCE-II) loss function by estimating the change of noise distribution for old data to guarantee no bias with respect to the retraining, (ii) a meta-optimization with deep reinforced Learning Rate Learning (LRL) mechanism which can adaptively learn the learning rate according to the status of the training processes and achieve fast convergence which is critical for incremental learning. Theoretically, the proposed ICL is equivalent to retraining, which is based on solid mathematical derivation. In practice, extensive experiments in different domains demonstrate that, without retraining a new model, ICL achieves up to 16.7x training speedup and 16.8x faster convergence with competitive results.
Graphs: Privacy and Generation through ML
Authors: Rucha Bhalchandra Joshi
Venue: WSDM
Year: 2023

Graphs are ubiquitous, which makes machine learning on graphs an important research area. While there are many aspects to this field, our research is focused primarily on two aspects of it. The first research question concerns privacy in graphs, where our work primarily focuses on preserving structural privacy in graphs. The second research question is about generating graphs. With applications in various fields such as drug discovery, designing novel proteins, etc., graph generation is emerging as an essential problem. This paper briefly describes the problems and the methodology to address them.
Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface
Authors: George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika
Venue: WSDM
Year: 2023

In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.