Biblio

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.

Nowadays, with such a huge amount of information available online, one key challenge is how to retrieve target data efficiently. A recent state-of-art solution, k-means hashing (KMH), codes data via a string of binary code obtained by iterative k-means clustering and binary code optimizing. To deal with high dimensional data, KMH divides the space into low-dimensional subspaces, places a hypercube in each subspace and finds its proper location by the mentioned optimizing process. However, the complexity of the optimization increases rapidly when the dimension of the hypercube increases. To address this issue, we propose an improved hashing method stacked k-means hashing (SKMH). The main idea is to increase the approximation by a coarse-to-fine multi-layer lower-dimensional cubes. With these kinds of lower-dimensional cubes, SKMH can achieve a similar approximation ability via a less optimizing time, compared with KMH method using higher-dimensional cubes. Extensive experiments have been conducted on two public databases, demonstrating the performance of our method by some common metrics in fast nearest neighbor search.

Nearest neighbor search algorithm plays a very important role in computer image algorithm. When the search data is large, we need to use fast search algorithm. The current fast retrieval algorithms are tree based algorithms. The efficiency of the tree algorithm decreases sharply with the increase of the data dimension. In this paper, a local integral hash nearest neighbor algorithm of the spatial space is proposed to construct the tree structure by changing the way of the node of the access tree. It is able to express data distribution characteristics. After experimental testing, this paper achieves more efficient performance in high dimensional data.