Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining最新文献

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations. 自带视图：无需预制数据增强的图形对比学习

Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining Pub Date : 2022-02-01 Epub Date: 2022-02-15 DOI: 10.1145/3488560.3498416

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

{"title":"Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations.","authors":"Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen","doi":"10.1145/3488560.3498416","DOIUrl":"10.1145/3488560.3498416","url":null,"abstract":"Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://github.com/Shen-Lab/GraphCL_Automated.","PeriodicalId":74530,"journal":{"name":"Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining","volume":"2022 ","pages":"1300-1309"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130056/pdf/nihms-1808195.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10730679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior. 在线行动与离线影响：在线社交网络如何影响在线和离线用户行为》（Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior.

Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining Pub Date : 2017-02-01 Epub Date: 2017-02-02 DOI: 10.1145/3018661.3018672

Tim Althoff, Pranav Jindal, Jure Leskovec

{"title":"Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior.","authors":"Tim Althoff, Pranav Jindal, Jure Leskovec","doi":"10.1145/3018661.3018672","DOIUrl":"10.1145/3018661.3018672","url":null,"abstract":"Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user's increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections.","PeriodicalId":74530,"journal":{"name":"Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining","volume":"2017 ","pages":"537-546"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361221/pdf/nihms846104.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34857331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporally Factorized Network Modeling for Evolutionary Network Analysis. 演化网络分析的时间分解网络建模。

Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining Pub Date : 2017-02-01 DOI: 10.1145/3018661.3018669

Wenchao Yu, Charu C Aggarwal, Wei Wang

{"title":"Temporally Factorized Network Modeling for Evolutionary Network Analysis.","authors":"Wenchao Yu, Charu C Aggarwal, Wei Wang","doi":"10.1145/3018661.3018669","DOIUrl":"https://doi.org/10.1145/3018661.3018669","url":null,"abstract":"The problem of evolutionary network analysis has gained increasing attention in recent years, because of an increasing number of networks, which are encountered in temporal settings. For example, social networks, communication networks, and information networks continuously evolve over time, and it is desirable to learn interesting trends about how the network structure evolves over time, and in terms of other interesting trends. One challenging aspect of networks is that they are inherently resistant to parametric modeling, which allows us to truly express the edges in the network as functions of time. This is because, unlike multidimensional data, the edges in the network reflect interactions among nodes, and it is difficult to independently model the edge as a function of time, without taking into account its correlations and interactions with neighboring edges. Fortunately, we show that it is indeed possible to achieve this goal with the use of a matrix factorization, in which the entries are parameterized by time. This approach allows us to represent the edge structure of the network purely as a function of time, and predict the evolution of the network over time. This opens the possibility of using the approach for a wide variety of temporal network analysis problems, such as predicting future trends in structures, predicting links, and node-centric anomaly/event detection. This flexibility is because of the general way in which the approach allows us to express the structure of the network as a function of time. We present a number of experimental results on a number of temporal data sets showing the effectiveness of the approach.","PeriodicalId":74530,"journal":{"name":"Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining","volume":"2017 ","pages":"455-464"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3018661.3018669","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35098607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

Improving Website Hyperlink Structure Using Server Logs. 利用服务器日志改进网站超链接结构。

Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining Pub Date : 2016-02-01 DOI: 10.1145/2835776.2835832

Ashwin Paranjape, Robert West, Leila Zia, Jure Leskovec

{"title":"Improving Website Hyperlink Structure Using Server Logs.","authors":"Ashwin Paranjape, Robert West, Leila Zia, Jure Leskovec","doi":"10.1145/2835776.2835832","DOIUrl":"https://doi.org/10.1145/2835776.2835832","url":null,"abstract":"Good websites should be easy to navigate via hyperlinks, yet maintaining a high-quality link structure is difficult. Identifying pairs of pages that should be linked may be hard for human editors, especially if the site is large and changes frequently. Further, given a set of useful link candidates, the task of incorporating them into the site can be expensive, since it typically involves humans editing pages. In the light of these challenges, it is desirable to develop data-driven methods for automating the link placement task. Here we develop an approach for automatically finding useful hyperlinks to add to a website. We show that passively collected server logs, beyond telling us which existing links are useful, also contain implicit signals indicating which nonexistent links would be useful if they were to be introduced. We leverage these signals to model the future usefulness of yet nonexistent links. Based on our model, we define the problem of link placement under budget constraints and propose an efficient algorithm for solving it. We demonstrate the effectiveness of our approach by evaluating it on Wikipedia, a large website for which we have access to both server logs (used for finding useful new links) and the complete revision history (containing a ground truth of new links). As our method is based exclusively on standard server logs, it may also be applied to any other website, as we show with the example of the biomedical research site Simtk.","PeriodicalId":74530,"journal":{"name":"Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining","volume":"2016 ","pages":"615-624"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2835776.2835832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34857330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 39