Depression is one of the leading factors in global disability and a top driver for suicides. Studies have shown that depression has an effect on language usage. In recent years, especially during the COVID pandemic, social media platforms have become the de facto platform for many individuals to self-disclose or discuss mental health issues like depression. This trend presents a unique opportunity for researchers and healthcare professionals to detect potential mental illnesses for early intervention or treatment by taking advantage of the recent advances in machine learning approaches. Existing depression detection methods on social media, however, suffer from two major limitations. First, these solutions heavily rely on the amount, quality, and type of user-posted content. Second, the overlooked social circle impact should be leveraged to enhance the prediction capabilities. In this paper, we propose a depression detection framework, MentalNet, based on heterogeneous graph convolution by capturing users’ interactions (replies, mentions, and quotetiveets) with their friends on social media and differentiating the intimacy of users’ social circles (e.g., family, friends, or acquaintances). Specifically, we formulate the problem of depression detection on social media as a graph classification problem by representing users’ social circles in the format of heterogeneous graphs. MentalNet embraces three modules, (1) extraction of ego-network node features, (2) construction of user interaction graphs, and (3) depression detection based on heterogeneous graph classification. The extensive experiments on Twitter data demonstrate that MentalNet consistently and significantly outperforms the state-of-the-art methods in terms of all the effectiveness metrics. Compared to the baseline methods, MentalNet is able to effectively predict early depression in Twitter users with up to 24% improvement on F1 score.

A fundamental problem that confronts virtual private network (VPN) applications is the overhead on throughput, ease of deployment and use, and overall utilization. WireGuard is a recently introduced light and secure cross-platform VPN application. It aims to simplify the process of setting up a secure connection while utilizing the multi-threading capability and minimizing the use of bandwidth. There have been several follow-up studies on WireGuard since its birth, most of which focus on the security analysis of the protocol. Despite the author's claim that WireGuard has impressive wins over OpenVPN and IPsec, there is no rigorous analysis of its performance to date. This paper presents a performance comparison of WireGuard and its main rival OpenVPN on various metrics. We construct an automated test framework and deploy it on a total of eight nodes, including remote AWS instances and local virtual machines. Our test results clearly show two main edges that WireGuard has over OpenVPN, its performance on multi-core machines and its light codebase.