Occupational identity concerns the self-image of an individual’s affinities and socioeconomic class, and directs how a person should behave in certain ways. Understanding the establishment of occupational identity is important to study work-related behaviors. However, large-scale quantitative studies of occupational identity are difficult to perform due to its indirect observable nature. But profile biographies on social media contain concise yet rich descriptions about self- identity. Analysis of these self-descriptions provides powerful insights concerning how people see themselves and how they change over time. In this paper, we present and analyze a longitudinal corpus recording the self-authored public biographies of 51.18 million Twitter users as they evolve over a six-year period from 2015-2021. In particular, we investigate the social approval (e.g., job prestige and salary) effects in how people self-disclose occupational identities, quantifying over-represented occupations as well as the occupational transitions w.r.t. job prestige over time. We show that self-reported jobs and job transitions are biased toward more prestigious occupations. We also present an intriguing case study about how self-reported jobs changed amid COVID-19 and the subsequent "Great Resignation" trend with the latest full year data in 2022. These results demonstrate that social media biographies are a rich source of data for quantitative social science studies, allowing unobtrusive observation of the intersections and transitions obtained in online self-presentation.

Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. The well-known local FwdPush algorithm[5] approximates PPVs and has a sublinear rate of O(1 over αε). A recent study [51] found that when high precision is required, FwdPush is similar to the power iteration method, and its run time is pessimistically bounded by O(m over α log 1 over ε). This paper looks closely at calculating PPVs for both directed and undirected graphs. By leveraging the linear invariant property, we show that FwdPush is a variant of Gauss-Seidel and propose a Successive Over-Relaxation based method, FwdPushSOR to speed it up by slightly modifying FwdPush. Additionally, we prove FwdPush has local linear convergence rate O(vol (S) over α log 1 over ε) enjoying advantages of two existing bounds. We also design a new local heuristic push method that reduces the number of operations by 10-50 percent compared to FwdPush. For undirected graphs, we propose two momentum-based acceleration methods that can be expressed as one-line updates and speed up non-acceleration methods by O (1 / √ α). Our experiments on six real-world graph datasets confirm the efficiency of FwdPushSOR and the acceleration methods for directed and undirected graphs, respectively.

Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this article, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two “embedding maps”. In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks (CNN) with kernels of different sizes simultaneously. A fully connected multi-layer perceptron (MLP) is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and rationality of our proposed method.

Tracking a targeted subset of nodes in an evolving graph is important for many real-world applications. Existing methods typically focus on identifying anomalous edges or finding anomaly graph snapshots in a stream way. However, edge-oriented methods cannot quantify how individual nodes change over time while others need to maintain representations of the whole graph all the time, thus computationally inefficient.

Digital media (including websites and online social networks) facilitate the broadcasting of news via flexible and personalized channels. Unlike conventional newspapers which become “read-only” upon publication, online news sources are free to arbitrarily modify news headlines after their initial release. The motivation, frequency, and effect of post-publication headline changes are largely unknown, with no offline equivalent from where researchers can draw parallels.

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges.