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bipartite_community_detection

collapsedkcore

anchoredKcore The code is for the CKC algorithm, published in the paper "Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem" Fan Zhang, Ying Zhang, Lu Qin, Wenjie Zhang, Xuemin Lin, AAAI 2017

community-detect

Community detection using attribute and structural similarities.

community-detection-alogrithms-evaluation

Different community detection algorithms has been evaluated in terms of time.

community-detection-twitter

dbscan

Python implementation of 'Density Based Spatial Clustering of Applications with Noise'

enhanced-knn

Enhanced KNN is an upgrade for the Conventional KNN Data mining classification method which can increase performance and optimization

eva

Eva: Community Discovery for Labeled Graphs (networkx implementation)

friendly-fortnight

:wolf: K-Means Clustering using Python from Scratch :mushroom:

graph-clustering-based-on-structural-attribute-similarities-

graph-community-detection

Market Segmentation using Attributed Graph Community Detection.

icde-kcore-budget

id-stopwords

Stopwords collection of Bahasa Indonesia collected from many sources.

market-segmentation-using-attributed-graph-community-detection

In this project, we aim to find such market segments given social network data. These social relations can be captured in a graph framework where nodes represent customers/users and edges represent some social relationship.

market-segmentation-using-attributed-graph-community-detection-1

Overview: Market segmentation divides a broad target market into subsets of consumers or businesses that have or are perceived to have common needs, interests, and priorities. These segments help firms or businesses focus on their target groups effectively and allocate resources efficiently. Traditional segmentation methods are solely based on attribute data such as demographics (age, sex, ethnicity, education, etc.) and psychographic profiles (lifestyle, personality, motives, etc.). However, social networks have recently become important for marketing. Depending on the nature of the market, social relations can even become vital in forming segments. Such social relations combined with demographic properties can be used to find more relevant subsets of consumers or businesses (i.e., communities).

mid-term-project-sna

Tugas Besar 1 (MID semester) Mata Kuliah Analisis Jejaring Sosial.

network_lesson

Lesson on Twitter Social Network Analysis

networkx-quick-start

From DataFrame to Network Graph

rcm

sentimentanalysis.social_media

Sentimen Analisis (tweet)

totemclust

ToTeM Complex networks clustering

twitter-community-detection

University project: Twitter community detection using Modularity, Infomap, Label propagation and Multilevel algorithms

twitter-friendship-graph

Community Detection on Twitter Friendship Graph

twitter-hate-speech-detection

A repository

twitter-sentiment-analysis

In today’s era, the impact of social networking media such as Facebook, Google Plus, YouTube, Blogs, and Twitter is increasing rapidly day by day. Millions of people are connected with each other on social networking sites and express their sentiments and opinions through tweets, and comments. This motivates the automatic mining and classification of views, emotions, opinions, and feelings of people on social networking websites. Sentiment analysis is the method of automatic mining of the sentiments or opinions of a text unit. In today’s world, people express their feelings, opinions on twitter about certain things i.e. event, topic or person. The proposed work deals with mining sentiments or emotions of tweets using Support Vector Machine. Unigram and TF-IDF are used as feature extractors and the performance of the proposed approach is measured in terms of accuracy, precision, recall, and f-measure.