Weighted congestion control aims to provide end-to-end differentiated bandwidth allocation. MulTCP and EWTCP are two closely related schemes for this purpose and they both want to achieve weighted proportionality through modifying AIMD behaviors. In this paper, we revisit the performance of MulTCP and EWTCP in terms of weighted proportionality. Through testbed experiments, we reveal a lot of counter-intuitive phenomena for achieving weighted proportionality — the switch buffer size, propagation delay and ACK options have a dominant impact on the weighted proportionality. Specifically, we develop WCC, a fundamental weighted AIMD-based congestion control building block, which can be implemented via individually modifying AI (WCC-AI) or MD (WCC-MD) behavior. We analyze WCC using extended fluid models, NS3 simulations and Linux kernel implementations with droptail and RED queues, and point out the determinant of performance. Finally, we clarify the influences of dynamic network characteristics on weighted proportionality with sufficient experimental results and summarize a basic law of how to implement weighted AIMD-based congestion control.

Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominate current EA research but still suffer from their reliance on labelled mappings. To solve this problem, a few works have explored boosting the training of EA models with self-training, which adds confidently predicted mappings into the training data iteratively. Though the effectiveness of self-training can be glimpsed in some specific settings, we still have very limited knowledge about it. One reason is the existing works concentrate on devising EA models and only treat self-training as an auxiliary tool. To fill this knowledge gap, we change the perspective to self-training to shed light on it. In addition, the existing self-training strategies have limited impact because they introduce either much False Positive noise or a low quantity of True Positive pseudo mappings. To improve self-training for EA, we propose exploiting the dependencies between entities, a particularity of EA, to suppress the noise without hurting the recall of True Positive mappings. Through extensive experiments, we show that the introduction of dependency makes the self-training strategy for EA reach a new level. The value of self-training in alleviating the reliance on annotation is actually much higher than what has been realised. Furthermore, we suggest future study on smart data annotation to break the ceiling of EA performance.