and (c) combined clas...Figure 7.6 Performance measurement index in graphical representation.
8 Chapter 8Figure 8.1 Overall methodology.Figure 8.2 Forecasting happy word.Figure 8.3 Forecasting “love” word.
9 Chapter 9Figure 9.1 Statista presented a ranking of social networks based on the number o...Figure 9.2 The semantic analysis framework in social media that transforms data ...Figure 9.3 Workflow of proposed system model.Figure 9.4 The user-side program’s activity diagram.Figure 9.5 Confusion matrix with 92.50% accuracy.Figure 9.6 Hashtag/keyword entry page.Figure 9.7 An examination of the effectiveness of positive and negative tweets.
10 Chapter 10Figure 10.1 Concept of graph theory.Figure 10.2 Connections in a graph.Figure 10.3 Linear threshold model. Source: https://snap-stanford.github.io/cs22...Figure 10.4 Independent cascade model.Figure 10.5 SIR model in python.Figure 10.6 SIR model graph showing susceptible, infected & recovered individual...Figure 10.7 Cascading behavior.Figure 10.8 Payoff matrix in coordination game.Figure 10.9 Payoff in cascading behavior.Figure 10.10 Keypeople in cascading behavior.Figure 10.11 Impact of communities on cascades.Figure 10.12 Cascading on clusters.
11 Chapter 11Figure 11.1 Nodes and edges in the graph.Figure 11.2 Nodes and edges in the social network.Figure 11.3 Python program for creating a symmetric social network using Network...Figure 11.4 Visualizing the symmetric social network created by using NetworkX.Figure 11.5 Python program for creating the asymmetric social network using Netw...Figure 11.6 Visualizing the asymmetric social network created by using NetworkX.Figure 11.7 Python program for creating an asymmetric social network by applying...Figure 11.8 Visualizing the asymmetric social network created after applying the...Figure 11.9 Implementing the weighted social network using NetworkX.Figure 11.10 Displaying the weighted network in the form of circular architectur...Figure 11.11 Developing the multigraph for social networks using G_graph.edges (...Figure 11.12 Results collected using the G_graph.edges () method.Figure 11.13 Degree of a node for symmetric social network.Figure 11.14 Clustering and average clustering of a node for symmetric graph.Figure 11.15 Calculating shortest path and shortest path length of a node.Figure 11.16 Implementing the breadth-first search algorithm for User C.Figure 11.17 Implementing the breadth-first search algorithm for User A.Figure 11.18 Eccentricity distribution of a node in a graph using the nx.eccentr...Figure 11.19 Centrality by Eigenvector using NetworkX () function.Figure 11.20 Nodes with a high degree of betweenness centrality.Figure 11.21 Closeness to all other nodes is displayed for the G_symmetric graph...Figure 11.22 Loading necessary packages and the dataset.Figure 11.23 Function info () to display the dataframe’s contents.Figure 11.24 Function info () to illustrate the nodes and edges in the data set.Figure 11.25 Degree_centrality () and nx.degree () functions.Figure 11.26 Average shortest path calculation between two networks.Figure 11.27 The draw_networkx () method to visualize the facebook data set.Figure 11.28 The visual representation of the facebook data set with draw_networ...Figure 11.29 Python code for betweenness_centrality ().Figure 11.30 The visual representation of the data set with betweenness_centrali...Figure 11.31 The sorted() method displays the nodes with the centrality.Figure 11.32 PageRank() method to estimate popularity.Figure 11.33 Popularity nodes according to the page rank() method.
Guide
1 Cover
5 Preface
7 Index
Pages
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