Community Detection in Networks with Node Attributes

Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.Comment: Published in the proceedings of IEEE ICDM '1

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202

It was nice to wake up from that one : an exploratory qualitative content analysis of vivid dreams and nightmares reported by people living with HIV/AIDS as side effects of Efavirenz

This is an exploratory, qualitative content analysis of 50 vivid dream and nightmare narratives posted to an online forum by people living with HIV/AIDS and taking the anti-HIV drug Efavirenz. It examines thematic connections among the dreams with consideration of how these themes might be linked to and reflective of complex subjective experiences of living with HIV/AIDS. This thesis demonstrates that the phenomenon of vivid dreams and nightmares as purported side effects of Efavirenz is of substantial interest to people living with HIV/AIDS. Furthermore, it argues that vivid dreams and nightmares experienced by people living with HIV/AIDS and taking Efavirenz are not simply medication side effects, but are meaningful experiences that are potentially useful in clinical social work with this population

Convergent flows: humanities scholars and their interactions with electronic texts

This article reports research findings related to converging formats, media, practices, and ideas in the process of academics’ interaction with electronic texts during a research project. The findings are part of the results of a study that explored interactions of scholars in literary and historical studies with electronic texts as primary materials. Electronic texts were perceived by the study participants as fluid entities because the electronic environment promotes seamless interactions with a variety of media and formats. Working with electronic texts combines some traditional information and research practices into new patterns of information behavior. The practice called “netchaining” combines aspects of networking with information-seeking practices to establish and shape online information chains, which link sources and people. Different forms of exploration of participants’ research questions were enabled by interactions with electronic texts

Gene × environment interaction by a longitudinal epigenome-wide association study (LEWAS) overcomes limitations of genome-wide association study (GWAS)

The goal of genome-wide association studies is to identify SNPs unique to disease. It usually involves a single sampling from subjects' lifetimes. While primary DNA sequence variation influences gene-expression levels, expression is also influenced by epigenetics, including the ‘somatic epitype’ (GSE), an epigenotype acquired postnatally. While genes are inherited, and novel polymorphisms do not routinely appear, GSE is fluid. Furthermore, GSE could respond to environmental factors (such as heavy metals) and to differences in exercise, maternal care and dietary supplements – all of which postnatally modify oxidation or methylation of DNA, leading to altered gene expression. Change in epigenetic status may be critical for the development of many diseases. We propose a ‘longitudinal epigenome-wide association study’, wherein GSE are measured at multiple time points along with subjects' histories. This Longitudinal epigenome-wide association study, based on the ‘dynamic’ somatic epitype over the ‘static’ genotype, merits further investigation

Newly Formed Cities: an AI Curation

Art curatorial processes are characterized by the presentation of a collection of artworks in a knowledgeable way. Machine processes are characterized by their capacity to manage and analyze large amounts of data. This paper envisages machine curation and audience interaction as a means to explore the implications of contemporary AI models for the curatorial world. This project was developed for the occasion of the 2023 Helsinki Art Biennial, entitled New Directions May Emerge. We use the Helsinki Art Museum (HAM) collection to re-imagine the city of Helsinki through the lens of machine perception. We use visual-textual models to place artworks currently hosted inside the museum in outdoor public spaces of the city, assigning fictional coordinates based on similarity scores. Synthetic 360{\deg} art panoramas are generated using diffusion-based models to propose a machinic visual style guided by the artworks. The result of this project will be virtually presented as a web-based installation, where such a re-contextualization allows the navigation of an alternative version of the city while exploring its artistic heritage. Finally, we discuss our contributions to machine curation and the ethical implications that such a process entails. The web-based installation is available at this link: http://newlyformedcity.com/

Anaġoptaŋ po! (Listen!) What We Can Learn About Our Own Stories by Accepting the Stories of Others: A Critical Discourse Analysis of Competing Narratives within the Dakota Access Pipeline Conflict

There have been over five-hundred years of interactions between European Colonizer-settlers and the Indigenous peoples of North America. Starting with the 1493 Doctrine of Discovery through the present, language embedded in documents, laws, policies and popular culture, have created damaging and misleading stereotypes and identities for these Indigenous Peoples, the American Indians. This study connects historical and contemporary perceptions constructing the dominant narrative that informs many people about American Indians. Narrative Paradigm Theory, Critical Race Theory and Indigenous Theories all serve as a lens to deconstruct the legitimacy of the dominant narrative and promote the salience of counter-narratives constructed by American Indians in their efforts to tell their own experience and declare their own identities. The construction of the Dakota Access Pipeline served as a flashpoint thrusting the narratives constructed by dominant culture, and American Indians, into the national and international consciousness. A critical discourse analysis of news reports of this event revealed the competing language, ideologies and worldviews held by those involved in the conflict, as well as consumers of the text and discourse

Inferring Dynamic User Interests in Streams of Short Texts for User Clustering

User clustering has been studied from different angles. In order to identify shared interests, behavior-based methods consider similar browsing or search patterns of users, whereas content-based methods use information from the contents of the documents visited by the users. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of streams of short texts. User clustering in this setting is more challenging than in the case of long documents, as it is difficult to capture the users’ dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (UCT). UCT adaptively tracks changes of each user’s time-varying topic distributions based both on the short texts the user posts during a given time period and on previously estimated distributions. To infer changes, we propose a Gibbs sampling algorithm where a set of word pairs from each user is constructed for sampling. UCT can be used in two ways: (1) as a short-term dependency model that infers a user’s current topic distribution based on the user’s topic distributions during the previous time period only, and (2) as a long-term dependency model that infers a user’s current topic distributions based on the user’s topic distributions during multiple time periods in the past. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed short-term and long-term dependency user clustering models compared to state-of-the-art baselines