论文标题
低级别表示复杂网络的分类问题
Low-Rank Representations Towards Classification Problem of Complex Networks
论文作者
论文摘要
已经广泛研究了代表社会相互作用，大脑活动，分子结构的复杂网络，以便能够理解和预测其特征作为图形。这些网络的模型和算法用于现实生活中的应用程序，例如搜索引擎和推荐系统。通常，这样的网络是通过构造网络顶点的低维欧几里德嵌入来建模的，该网络的嵌入在欧几里得空间中的顶点接近，这暗示了边缘的可能性（链接）。在这项工作中，我们研究了现实生活网络对网络分类问题的这种低级表示的性能。
Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constructing a low-dimensional Euclidean embedding of the vertices of the network, where proximity of the vertices in the Euclidean space hints the likelihood of an edge (link). In this work, we study the performance of such low-rank representations of real-life networks on a network classification problem.
