GraphQLObjectType({ name: 'Query', fields: { // 一个个查询方法 getSuperHero: { type: HeroType, args: { heroName: { type: graphql.GraphQLString } }, // 方法实现 查询的处理函数 resolve: function(_, { heroName }){ const name = heroName const age = 18 return { name, age } }...
computational devices, the flow of computation, etc. For instance, the link structure of a website can be represented by a directed graph, in which the vertices represent web pages and directed edges represent links from one page to another. A...
知识图谱目前的研究方向可以大致分为四类:知识表征学习(Knowledge Represent Learning, KRL)、知识获取(Knowledge Acquisition)、时序知识图谱(Temporal Knowledge Graph, TKG)和应用(Knowledge-aware Applications)。 图3展示了知识图谱的主要研究方向分支图,图中详细罗列了相关领域的承继关系。 图3 知识图谱的主要研究方向...
It's suitable for making one off requests.To represent the session in the API, use the workbook-session-id: {session-id} header.Note: The session header is not required for an Excel API to work. However, we recommend that you use the session header to improve performance. If you don'...
GraphLab was developed by Carnegie Mellon University and provides an example of graph-parallel distributed analytics engines for the cloud. As with any graph-parallel engine, GraphLab assumes input problems modeled as graphs, in which vertices represent computations and edges encode data dependencies o...
@superbrother答案从傅立叶变换出发到拉普拉斯矩阵最后再到GCN已经很详细和全面的讲解，清晰明确非常赞 。
Delegated permissions, also referred to asscopes: The permissions exposed by Microsoft Graph and that represent the operations that the app can perform on behalf of the signed-in user. The app might be allowed to perform an operation on behalf of one user but not another. ...
We then compute the Hadamard distance between two node features to represent the edge vector, as is shown in Eq. (5). Once edge vectors are obtained, an active function sigmoid \(\sigma\) is applied to evaluate the edge existing probability between nodes. We initialize the parameter of GAN...
outputs relate to the edge classification andlink prediction tasks. With two nodes’ hidden representa-tions from GNNs as inputs, a similarity function or a neu-ral network can be utilized to predict the label/connectionstrength of an edge ...
Figure 3. Sampling analysis: (a,b); visual nodes represent the update process: (c). Figure 4. Visualization of node distribution at different training phases. Completing the above preparations, let us next explore how Skip-gram works. In the initial stage of learning, as shown in line ...