Logistic Regression: individual behavior based on demographics can be used to predict the likelihood of a category of actions from that individual. It can also be used for finding repetitions in actions. This is a form of regression that supports binary outcomes. It uses statistical measures, is highly flexible, takes any kind of input and supports different analytical tasks. This regression folds the effects of extreme values and evaluates several factors that affects a pair of outcomes.
This is widely used method for machine learning involving neurons that have one or more gates for input and output. Each neuron assigns a weight usually based on probability for each feature and the weights are normalized across resulting in a weighted matrix that articulates the underlying model in the training dataset. Then it can be used with a test data set to predict the outcome probability. Neurons are organized in layers and each layer is independent of the other and can be stacked so they take the output of one as the input to the other. All messages written by individuals on social engineering applications can now be evaluated with SoftMax NLP classifiers for detecting keywords
Naïve Bayes is widely used for cases where conditions apply especially binary conditions such as with or without. If the input variables are independent, their states can be calculated as probabilities, and there is at least a predictable output, this algorithm can be applied. The simplicity of computing states by counting for class using each input variable and then displaying those states against those variables for a give value, makes this algorithm easy to visualize, debug and use as a predictor.
Collaborative filtering Recommendations include suggestions for knowledge base, or to find model service requests. In order to make a recommendation, first a group sharing similar taste is found and then the preferences of the group are used to make a ranked list of suggestions. This technique is called collaborative filtering. A common data structure that helps with keep tracking of people and their preferences is a nested dictionary. This dictionary could use a quantitative ranking say on a scale of 1 to 5 to denote the preferences of the people in the selected group. To find similar people to form a group, we use some form of a similarity score. One way to calculate this score is to plot the items that the people have ranked in common and use them as axes in a chart. Then the people who are close together on the chart can form a group.
Conclusion: There are several other mining algorithms that can be listed along with those above for their usages on social graphs. This is just a precedence for applications such as chatbots and assistants to find useful and relevant information for individuals or businesses from the social graph.
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