A graph G(V,E) is an ǫ-sparsification of G for some ǫ > 0, if every (weighted) cut in G is within (1 ± ǫ) of the corresponding cut in G. A celebrated result of Benczúr and Karger shows that for every undirected graph G, an ǫ-sparsification with O(n log n/ǫ) edges can be constructed in O(m log n) time. The notion of cut-preserving graph sparsification has played an important role in speeding up ...

Perkembangan teknologi informasi yang kian cepat membuat masyarakat mencoba hal – baru dalam berbelanja di dunia maya. Dengan semakin populernya live streaming, banyak vendor platform social commerce telah mengadopsi streaming sebagai alat untuk meningkatkan kinerja penjualan khususnya Indonesia. Baru ini ditemukan fenomena bahwa shopping dilakukan salah satu sosial media TikTok sedang menanjak...

We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reco...

Previous studies have paid little attention to spectators’ consumption behaviors and motives for watching different types of esports live-streaming game genres. This study, therefore, investigates spectator based on the interaction effects Convenience sampling was conducted collect 312 responses from Taiwanese individuals via Professional Technology Temple. The measurement tools include motivat...

Sparsfying matrices is a ubiquitous operation in large scale machine learning, data mining and signal processing. More formally, given a large matrix A, we aim to find another matrix B, such that }A B} ¤ ε with B being significantly sparser than A. Using B as a surrogate for A is more efficient and often provides provably good approximations for many tasks. In this paper, we suggest an element-...

More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, u...

A spectral sparsifier of a graph G is a sparser graph H that approximately preserves the quadratic form of G, i.e. for all vectors x, xLGx ≈ x T LHx, where LG and LH denote the respective graph Laplacians. Spectral sparsifiers generalize cut sparsifiers, and have found many applications in designing graph algorithms. In recent years, there has been interest in computing spectral sparsifiers in ...

A conditional sampling oracle for a probability distribution D returns samples from the conditional distribution of D restricted to a specified subset of the domain. A recent line of work [CFGM13, CRS14] has shown that having access to such a conditional sampling oracle requires only polylogarithmic or even constant number of samples to solve distribution testing problems like identity and unif...

We study the problem of estimating the number of triangles in a graph stream. No streaming algorithm can get sublinear space on all graphs, so methods in this area bound the space in terms of parameters of the input graph such as the maximum number of triangles sharing a single edge. We give a sampling algorithm that is additionally parameterized by the maximum number of triangles sharing a sin...