The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.

Abstract Mobile applications have grown rapidly in size. This dramatic increases in size and complexity make mobile applications less accessible to a broader scope of users. The prevailing approach for better accessibility of mobile applications is to manually reimplement slimmed versions with a small but representative portion of a regular original app. Unfortunately, this approach imposes significant burden on developers. We propose a system called Madusa to enable developers to effectively customize and reduce their mobile applications for Android. Madusa takes as input an original app, an upper bound on the size of a reduced version, and usage scenarios as a high-level specification of its desired core functionality. The output is a reduced version of the app that is still correct with respect to the specification while not exceeding the size limit. Madusa constructs a graph representing dependencies among methods and resources and identifies a sub-part of the graph using integer linear programming to generate a reduced version that exhibits behaviors as similar as possible to the original app. Our experimental evaluation on a suite of 19 Android apps available on Google Play Store. Madusa effectively converges to the desired simplified apps by reducing the app size by 40% on average (maximally by 60%). We conclude our approach effectively removes redundant code and resources with respect to given usage scenarios.

The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal dynamics of the factual information. In this paper, we posit that the intrinsic difficulty of existing TKGE methods lies in the lack of information in KG snapshots with timestamps, each of which contains the facts that co-occur at a specific timestamp. To address this challenge, we propose a novel self-supervised TKGE approach, THOR (Three-tower grapH cOnvolution netwoRks (GCNs)), which extracts latent knowledge from TKGs by jointly leveraging both temporal and atemporal dependencies between entities and the structural dependency between relations. THOR learns the embeddings of entities and relations Our experiments on three real-world datasets demonstrate that THOR significantly outperforms 13 competitors in terms of TKG completion tasks. The codebase of THOR is available at https://github.com/EJHyun/THOR.