David Naori

2papers

2 Papers

20.7DSMay 10
Dynamic Edge Coloring of Forests

Haim Kaplan, David Naori, Yaniv Sadeh

In the \emph{dynamic edge coloring} problem, one has to maintain a graph of maximum degree $Δ$ with at most $Δ+c$ colors, given updates to the edges of the graph. An important objective is to minimize the \emph{recourse}, which is the number of edges being recolored. We study this problem on forests, which is a natural yet nontrivial restriction of the problem. We consider the problem in both \emph{incremental} (edges are only inserted) and \emph{fully dynamic} (edges may be deleted) models. In the deterministic setting, we show that the natural greedy algorithm achieves $O(\frac{1}{c + \sqrtΔ})$ amortized recourse in the incremental model, and this is tight up to tie-breaking. In contrast, in a fully dynamic forest, greedy can be forced to have $Ω(\log_Δn)$ amortized recourse. To partially alleviate this limitation of greedy, we show an optimal non-greedy algorithm with $O(1)$ amortized recourse for \emph{rooted} fully dynamic forests and $c = Δ- 2$. In the randomized setting, we give a natural distribution-maintaining algorithm that achieves $Θ(\frac{1}Δ)$ expected amortized recourse in the incremental model and $Θ(\min \{ \fracΔ{c}, \log_Δ n \})$ expected recourse in the dynamic model. These randomized results are optimal for $c=0$.

CLNov 4, 2020
Answer Identification in Collaborative Organizational Group Chat

Naama Tepper, Naama Zwerdling, David Naori et al.

We present a simple unsupervised approach for answer identification in organizational group chat. In recent years, organizational group chat is on the rise enabling asynchronous text-based collaboration between co-workers in different locations and time zones. Finding answers to questions is often critical for work efficiency. However, group chat is characterized by intertwined conversations and 'always on' availability, making it hard for users to pinpoint answers to questions they care about in real-time or search for answers in retrospective. In addition, structural and lexical characteristics differ between chat groups, making it hard to find a 'one model fits all' approach. Our Kernel Density Estimation (KDE) based clustering approach termed Ans-Chat implicitly learns discussion patterns as a means for answer identification, thus eliminating the need to channel-specific tagging. Empirical evaluation shows that this solution outperforms other approached.