OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning Framework for Self-evolving Hierarchical Community Detection
Authors: Daizong Ding, Mi Zhang, Hanrui Wang, Xudong Pan, Min Yang, Xiangnan He
Venue: CIKM
Year: 2021

Hierarchical community detection, which aims at discovering the hierarchical structure of a graph, attracts increasing attention due to its wide range of applications. However, due to the difficulty of parametrizing the community tree, existing methods mainly rely on heuristic algorithms, which are limited by their low accuracy and inability to handle new observations. As far as we know, how to leverage deep learning techniques to better discover hierarchical communities remains almost blank in the existing literature. In this paper, we present the first deep learning framework called ReinCom for hierarchical community detection. To address the challenge of parametrizing the community tree, we propose a novel growing-up process where, at each step, we first partition nodes into the community tree and then adjust the community tree according to the partition results. To learn an optimal growing-up process, we propose an embedding agent and a community agent to implement the two sub-steps respectively. Furthermore, we also propose an online learning strategy for new observations on the graph. Empirical results show that our proposed model has better modeling effectiveness than the state-of-the-art methods. For example, in terms of modularity, the performance of ReinCom is 33% higher than previous community detection works. Besides, with the aid of the learned node embeddings, we also devise a graph visualization algorithm which can consistently reflect the latent hierarchical structure of a graph.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Authors: Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li
Venue: CIKM
Year: 2021

Graph Neural Networks have recently become a prevailing paradigm for various high-impact graph analytical problems. Existing efforts can be mainly categorized as spectral-based and spatial-based methods. The major challenge for the former is to find an appropriate graph filter to distill discriminative information from input signals for learning. Recently, myriads of explorations are made to achieve better graph filters, e.g., Graph Convolutional Network (GCN), which leverages Chebyshev polynomial truncation to seek an approximation of graph filters and bridge these two families of methods. Nevertheless, it has been shown in recent studies that GCN and its variants are essentially employing fixed low-pass filters to perform information denoising. Thus their learning capability is rather limited and may over-smooth node representations at deeper layers. To tackle these problems, we develop a novel graph neural network framework AdaGNN with a well-designed adaptive frequency response filter. At its core, AdaGNN leverages a simple but elegant trainable filter that spans across multiple layers to capture the varying importance of different frequency components for node representation learning. The inherent differences among different feature channels are also well captured by the filter. As such, it empowers AdaGNN with stronger expressiveness and naturally alleviates the over-smoothing problem. We empirically validate the effectiveness of the proposed framework on various benchmark datasets. Theoretical analysis is also provided to show the superiority of the proposed AdaGNN. The open-source implementation of AdaGNN can be found here: https://github.com/yushundong/AdaGNN.
Reevaluating the Change Point Detection Problem with Segment-based Bayesian Online Detection
Authors: Erick Draayer, Huiping Cao, Yifan Hao
Venue: CIKM
Year: 2021

Change point detection is widely used for finding transitions between states of data generation within a time series. Methods for change point detection currently assume this transition is instantaneous and therefore focus on finding a single point of data to classify as a change point. However, this assumption is flawed because many time series actually display short periods of transitions between different states of data generation. Previous work has shown Bayesian Online Change Point Detection (BOCPD) to be the most effective method for change point detection on a wide range of different time series. This paper explores adapting the change point detection algorithms to detect abrupt changes over short periods of time. We design a segment-based mechanism to examine a window of data points within a time series, rather than a single data point, to determine if the window captures abrupt change. We test our segment-based Bayesian change detection algorithm on 36 different time series and compare it to the original BOCPD algorithm. Our results show that, for some of these 36 time series, the segment-based approach for detecting abrupt changes can much more accurately identify change points based on standard metrics.
THECOG - Transforms In Behavioral And Affective Computing
Authors: Georgios Drakopoulos, Eleanna Kafeza
Venue: CIKM
Year: 2021

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.
DistRDF2ML - Scalable Distributed In-Memory Machine Learning Pipelines for RDF Knowledge Graphs
Authors: Carsten Felix Draschner, Claus Stadler, Farshad Bakhshandegan Moghaddam, Jens Lehmann, Hajira Jabeen
Venue: CIKM
Year: 2021

This paper presents DistRDF2ML, the generic, scalable, and distributed framework for creating in-memory data preprocessing pipelines for Spark-based machine learning on RDF knowledge graphs. This framework introduces software modules that transform large-scale RDF data into ML-ready fixed-length numeric feature vectors. The developed modules are optimized to the multi-modal nature of knowledge graphs. DistRDF2ML provides aligned software design and usage principles as common data science stacks that offer an easy-to-use package for creating machine learning pipelines. The modules used in the pipeline, the hyper-parameters and the results are exported as a semantic structure that can be used to enrich the original knowledge graph. The semantic representation of metadata and machine learning results offers the advantage of increasing the machine learning pipelines' reusability, explainability, and reproducibility. The entire framework of DistRDF2ML is open source, integrated into the holistic SANSA stack, documented in scala-docs, and covered by unit tests. DistRDF2ML demonstrates its scalable design across different processing power configurations and (hyper-)parameter setups within various experiments. The framework brings the three worlds of knowledge graph engineers, distributed computation developers, and data scientists closer together and offers all of them the creation of explainable ML pipelines using a few lines of code.
AutoCombo: Automatic Malware Signature Generation Through Combination Rule Mining
Authors: Min Du, Wenjun Hu, William Hewlett
Venue: CIKM
Year: 2021

Malware detection is an essential step in building trustworthy computer systems. Signature-based detection detects a sample as malware if the sample data match or contain a pre-stored malware signature. Among all detection methods that malware experts are constantly exploring, signature-based malware detection is indispensable, due to its simplicity, explainability and efficiency. Malware signatures could have various formats, for example, a substring, a subsequence, or a combination rule. A combination rule signature could be viewed as a fixed set of properties, each of which describes some characteristic of an analyzed sample. Although security experts have dedicated many efforts to extract meaningful features from samples, the step of signature generation from the features has been rather ad hoc and time-consuming. This paper focuses on the generation of combination rule signatures. We abstract and formally define the problem of combination rule malware signature generation, followed by a systematic study towards an effective and efficient implementation. Inspired by classic frequent itemsets mining solutions, the proposed AutoCombo approach is greedy but also complete. It generates higher quality signatures first, but is also able to traverse all possible property combinations for a complete generation. Further optimizations and future research potential are also discussed. The proposed approach is currently in use to assist the analysis for millions of files per day in a large security company. Our evaluation results using large-scale production data have also shown its efficacy. With the release of over 10 million real production records as well as our exploratory code, we hope this initial study could draw AI experts' attention and advance the research even further in this field.
From Pixels to Words: A Scalable Journey of Text Information from Product Images to Retail Catalog
Authors: Pranay Dugar, Rajesh Shreedhar Bhat, Asit Sharad Tarsode, Uddipto Dutta, Kunal Banerjee, Anirban Chatterjee, Vijay Srinivas Agneeswaran
Venue: CIKM
Year: 2021

Extracting texts of various shapes, sizes, and orientations from images containing multiple objects is an important problem in many contexts, especially, in connection to E-commerce. In the context of the scale at which Walmart operates, the text from an image can be a richer and more accurate source of data than human inputs and can be used in several applications such as Attribute Extraction, Offensive Text Classification, Product Matching among others. The motivation of this particular work has come from different business requirements such as flagging products whose images contain words that are non-compliant with organizational policies and building an efficient automated system to identify similar products by comparing the information contained in their respective product images and many others. Existing methods fail to address domain specific challenges like high entropy, different orientations, and small texts in product images adequately. In this work, we provide a solution that not only addresses these challenges but is also proven to work at a million image scale for various retail business units within Walmart. Extensive experimentation revealed that our proposed solution has been able to save around 30% computational cost in both the training and the inference stages.
Fine and Coarse Granular Argument Classification before Clustering
Authors: Lorik Dumani, Tobias Wiesenfeldt, Ralf Schenkel
Venue: CIKM
Year: 2021

Computational argumentation and especially argument mining together with retrieval enjoys increasing popularity. In contrast to standard search engines that focus on finding documents relevant to a query, argument retrieval aims at finding the best supporting and attacking premises given a query claim, e.g., from a predefined collection of arguments. Here, a claim is the central part of an argument representing the standpoint of a speaker with the goal to persuade the audience, and a premise serves as evidence to the claim. In addition to the actual retrieval process, existing work has focused on (1) classifying polarities of arguments into supporting or opposing, (2) classifying arguments by their frames (such as economic or environmental), and (3) clustering similar arguments by their meaning to avoid repetitions in the result list. For experiments, either hand-made argument collections or arguments extracted from debate portals were used. In this paper, we extend existing work on argument clustering, making the following contributions: First, we introduce a novel pipeline for clustering arguments. While previous work classified arguments either by polarity, frame, or meaning, our pipeline incorporates these three, allowing a more systematic presentation of arguments. Second, we introduce a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers. A thorough evaluation with this dataset provides a first baseline for future work.
Beast: Scalable Exploratory Analytics on Spatio-temporal Data
Authors: Ahmed Eldawy, Vagelis Hristidis, Saheli Ghosh, Majid Saeedan, Akil Sevim, A. B. Siddique, Samriddhi Singla, Ganesh Sivaram, Tin Vu, Yaming Zhang
Venue: CIKM
Year: 2021

This paper introduces the open-source Beast system for scalable exploratory data science on big spatio-temporal data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatio-temporal data. Beast provides a set of extensible components that naturally integrate with Spark to build exploratory data science pipelines. Beast can install in less than a minute on an existing Spark cluster and provides a wide array of features including loading vector and raster data represented in standard file formats, synthetic data generation for benchmarking, load-balanced spatial partitioning, data summarization, interactive visualization, and more. Beast builds on several research projects; its goal is to make all this research widely available to researchers in one integrative and coherent system.
Collaborative Experts Discovery in Social Coding Platforms
Authors: Roohollah Etemadi, Morteza Zihayat, Kuan Feng, Jason Adelman, Ebrahim Bagheri
Venue: CIKM
Year: 2021

The popularity of online social coding (SC) platforms such as GitHub is growing due to their social functionalities and tremendous support during the product development lifecycle. The rich information of experts' contributions on repositories can be leveraged to recruit experts for new/existing projects. In this paper, we define the problem of collaborative experts finding in SC platforms. Given a project, we model an SC platform as an attributed heterogeneous network, learn latent representations of network entities in an end-to-end manner and utilize them to discover collaborative experts to complete a project. Extensive experiments on real-world datasets from GitHub indicate the superiority of the proposed approach over the state-of-the-art in terms of a range of performance measures.
OpenAttHetRL: An Open Source Toolkit for Attributed Heterogeneous Network Representation Learning
Authors: Roohollah Etemadi, Morteza Zihayat, Kuan Feng, Jason Adelman, Ebrahim Bagheri
Venue: CIKM
Year: 2021

Learning the latent representations of entities based on their relationships and the data associated with them is an essential task in many applications such as ranking, recommendation systems, graph-based team formation, keyword search, and many more. However, the majority of existing techniques learn the latent representations of either network or textual data. Structural embedding techniques suffer from the sparsity of real-world networks. Attributes of nodes are a source of rich information to ameliorate network embedding vectors which are overlooked in the literature. Thus, most existing network representation learning tools capture structural information. This paper introduces an open-source toolkit called OpenAttHetRL to learn the latent representations of entities based on their both network and textual data in an end-to-end fashion. OpenAttHetRL is easy to employ and adapt for a variety of tasks including ranking, recommendation systems, and expert finding. OpenAttHetRL aims to provide a unified toolkit for data pre-processing, building and training models, and performing predictions for a downstream task. It employs a graph convolution network to capture the relationships among entities and a kernel pooling technique to preserve the similarity of their textual data in the embedding space. We use expert finding in community question answering systems to demonstrate how OpenAttHetRL can be trained to get latent representations of questions, their askers, tags, and answerers and find potential answerers of new questions.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Authors: Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, Philip S. Yu
Venue: CIKM
Year: 2021

In order to model the evolution of user preference, we should learn user/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendation~(SR) problem. Existing methods leverage sequential patterns to model item transitions. However, most of them ignore crucial temporal collaborative signals, which are latent in evolving user-item interactions and coexist with sequential patterns. Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging. Firstly, it is hard to simultaneously encode sequential patterns and collaborative signals. Secondly, it is non-trivial to express the temporal effects of collaborative signals. Hence, we design a new framework Temporal Graph Sequential Recommender (TGSRec) upon our defined continuous-time bipartite graph. We propose a novel Temporal Collaborative Transformer TCT layer in TGSRec, which advances the self-attention mechanism by adopting a novel collaborative attention. TCT layer can simultaneously capture collaborative signals from both users and items, as well as considering temporal dynamics inside sequential patterns. We propagate the information learned from TCT layer over the temporal graph to unify sequential patterns and temporal collaborative signals. Empirical results on five datasets show that modelname significantly outperforms other baselines, in average up to 22.5% and 22.1% absolute improvements in Recall@10 and MRR, respectively.
An RDF Data Management System for Conflict Casualties
Authors: Yad Fatah, Mark Nourallah, Lynn Wahab, Fatima K. Abu Salem, Shady Elbassuoni
Venue: CIKM
Year: 2021

In a world embroiled in armed conflicts, documenting conflict casualties is an important goal for many NGOs. Most of such documented records of casualties are however managed through internal databases, spreadsheets or Web forms. As such, exploring and querying such data becomes extremely chaotic. In this paper, we demonstrate CasualtIS, an RDF data management system for conflict casualties. Our system models conflict casualties data as RDF graphs and allows users to query such data using a SPARQL endpoint. Our system also includes a template-based natural-language querying interface to support non-expert users. Our system can be used for various purposes by end users, such as fact-checking certain claims about conflict casualties, aggregating casualties over time and location, and finding contextual information about casualties, such as the cause of death, actors involved, and other similar critical information. We demonstrate our system using two case studies, one related to casualties in the Iraqi war and the other related to casualties in the Syrian war.
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Authors: Xidong Feng, Chen Chen, Dong Li, Mengchen Zhao, Jianye Hao, Jun Wang
Venue: CIKM
Year: 2021

Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples. Though with significant performance improvement, it commonly suffers from two critical issues: the non-compatibility with mainstream industrial deployment and the heavy computational burdens, both due to the inner-loop gradient operation. These two issues make them hard to be applied in practical recommender systems. To enjoy the benefits of meta learning framework and mitigate these problems, we propose a recommendation framework called Contextual Modulation Meta Learning (CMML). CMML is composed of fully feed-forward operations so it is computationally efficient and completely compatible with the mainstream industrial deployment. CMML consists of three components, including a context encoder that can generate context embedding to represent a specific task, a hybrid context generator that aggregates specific user-item features with task-level context, and a contextual modulation network, which can modulate the recommendation model to adapt effectively. We validate our approach on both scenario-specific and user-specific cold-start setting on various real-world datasets, showing CMML can achieve comparable or even better performance with gradient based methods yet with higher computational efficiency and better interpretability.
Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems
Authors: Philip J. Feng, Pingjun Pan, Tingting Zhou, Hongxiang Chen, Chuanjiang Luo
Venue: CIKM
Year: 2021

User behavior has been validated to be effective in revealing personalized preferences for commercial recommendations. However, few user-item interactions can be collected for new users, which results in a nullspace for their interests, ie, the cold-start dilemma. In this paper, a two-tower framework, namely, the model-agnostic interest learning (MAIL) framework, is proposed to address the cold-start recommendation (CSR) problem for recommender systems. In MAIL, one unique tower is constructed to tackle the CSR from a zero-shot view, and the other tower focuses on the general ranking task. Specifically, the zero-shot tower first performs cross-modal reconstruction with dual autoencoders to obtain virtual behavior data from highly aligned hidden features for new users; and the ranking tower can then output recommendations for users based on the completed data by the zero-shot tower. Practically, the ranking tower in MAIL is model-agnostic and can be implemented with any embedding-based deep models. Based on the cotraining of the two towers, the MAIL presents an end-to-end method for recommender systems that shows an incremental performance improvement. The proposed method has been successfully deployed on the live recommendation system of NetEase Cloud Music to achieve a click-through rate improvement of 13% to 15% for millions of users. Offline experiments on real-world datasets also show its superior performance in CSR. Our code is available.