# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 5 Mis-Configuration Detection Model
### Model Overview  
We propose a unified model which integrates the deep learning technologies with classic machine learning classification algorithms to identify the mis-configured author profiles. The model architecture is shown in Figure 2.  
In a nutshell, the model has four modules. First, we leverage TextCNN [21], which is known to be good at extracting local features from texts, for paper title encoding as it is shown in the green box (Section 5.2). Then the latent features of all the paper titles in a paper list are sorted into a sequence. The sequential features are captured using a RNN-based method, CW-RNN [22], and are aggregated by a paper attention-based aggregator. The process is shown in the purple box (Section 5.3). Moreover, in the orange box, the social feature of each scholar is extracted from the co-author graph (Section 5.4). The social graph embedding is obtained using a state-of-the-art method for node embedding, i.e., node2 vec [23]. Finally, the aggregated feature of the paper list, the social graph embedding and the selected features mentioned in Section 4.1 are fed into a supervised machine learning-based classifier to find whether the author profile is mis-configured or reliable (Section 5.5). The pseudocode of the model training and test process is shown as Algorithm 1. We will introduce the four modules, respectively, in detail in the following subsections.  
```
0: paper lists of all the author profiles: \(X_{paper\_list}\) (the paper list of each author profile consists of several paper entries), co-author graph: G, selected features of all the author profiles: \(X_{selected\_features}\), the maximum number of papers in the paper list: \(n_{a}\)
0: Mis-configuration classification result of each user: _res_
1: Initialize parameters
2:for each training iteration do
3: Sample a batch of training data
4:for\(X_{paper\_list}[i]\in X_{paper\_list}[batch]\)do
5:for\(paper\_entry[j]\in X_{paper\_list}[i]\)do
6:\(paper\_embedding[i][j]\gets TextCNN(paper\_entry[j].title)\)
7:endfor
8:endfor
9:\(paper\_list\_feature[batch]\leftarrow\) CW-RNN\((paper\_embedding[batch][1\ldots n_{a}])\)
10:\(social\_embedding[batch]\gets Node2 vec(G)\)
11:\(feature[batch]\gets paper\_list\_feature[batch]\oplus social\_embedding[batch]\)
12:\(feature[batch]\gets feature[batch]\bigoplus X_{selected\_features}[batch]\)
13:\(nn\_res[batch]\gets Fully\_Connected\_NN(feature[batch])\)
14: Update the neural network parameters
15:\(res[batch]\gets LightGBM(feature[batch])\)
16: Update the LightGBM parameters
17:endfor
18:for each test iteration do
19: Sample a batch of testing data
20:for\(X_{paper\_list}[i]\in X_{paper\_list}[batch]\)do
21:for\(paper\_entry[j]\in X_{paper\_list}[i]\)do
22:\(paper\_embedding[i][j]\gets TextCNN(paper\_entry[j].title)\)
23:endfor
24:endfor
25:\(paper\_list\_feature[batch]\leftarrow\) CW-RNN\((paper\_embedding[batch][1\ldots n_{a}])\)
26:\(social\_embedding[batch]\gets Node2 vec(G)\)
27:\(feature[batch]\gets paper\_list\_feature[batch]\oplus social\_embedding[batch]\)
28:\(feature[batch]\gets feature[batch]\bigoplus X_{selected\_features}[batch]\)
29:\(res[batch]\gets LightGBM(feature[batch])\)
30:endfor
```  
**Algorithm 1** The pseudocode of the mis-configuration detection model.  
Figure 2: The architecture of the model. (1) The green box (box 1) is the process of paper title encoding where TextCNN is leveraged. (2) The paper attention-based aggregator is shown in the purple box (box 2), where the hidden features of different papers are produced by CW-RNN and aggregated with different weights. (3) The orange box (box 3) shows the process to obtain the author embedding from the co-author graph. (4) The blue box (box 4) is a machine learning classifier (we use LightGBM in this paper) to classify the author profiles based on the concatenated features. (5) The output of the models is the judgment of whether an author profile is mis-configured or not.