Coping with the Loss of a Cherished Friendship: Advice for Healing and Moving Forward • Llego.dev

Dear friends, I want to share some thoughts with you today, drawn from my own experiences, with the hope of offering solace if you’ve endured the sudden end of a cherished friendship. Losing a friend can be a deeply painful experience.

I remember the day my closest friend and I drifted apart like it was yesterday. The feelings of surprise, disappointment, and longing were overwhelming, but they became the foundation for the wisdom I now share.

The Initial Shock: Surprise

The first wave to hit was sheer surprise. It felt like a thunderstorm on a sunny day. How could someone so integral to my life suddenly vanish? Confusion, betrayal, and anger surfaced. I questioned my actions and what I could have done differently.

But surprise is a fleeting state. It’s a shock that forces us to face a new reality. And the truth is often messy and unpredictable. People and circumstances change, and sometimes friendships do too. Accepting this, though difficult, is a crucial first step.

The Weight of What Was: Disappointment

Disappointment then settled in, heavy like a fog. Unanswered questions and unfulfilled promises felt unbearable. Remember, feeling disappointment is natural. It signifies that you cared, invested, and hoped. Your expectations weren’t inherently wrong; they were signs of trust and respect.

However, expectations can become unrealistic. We might expect ourselves, others, or situations to remain static. We might expect others to understand our unexpressed needs. Sometimes, we expect perfection.

But people are imperfect, just like us. We all have flaws and limitations. We make mistakes, hurt others, and experience hurt ourselves. Our differing needs, wants, and goals can create conflicts leading to damaged or ended friendships.

How to Cope with Disappointment: Acknowledge the feeling, express it in a healthy way (perhaps through journaling or talking to a trusted friend), and then actively work on letting it go. Forgive yourself and the other person for being human. Learn from the experience, adjust your expectations to be more flexible and realistic, and practice gratitude for the good times and the lessons learned.

The Lingering Ache: Longing

The longing was the persistent echo, an ache that refused to fade. This longing is a testament to the depth of your connection, a part of the journey you must navigate. It’s normal to miss someone who was once close, to reminisce about shared joys, and to wish things were different.

However, longing can become unhealthy if it becomes obsessive, hindering your ability to move forward and enjoy the present. If you find yourself stuck in the past, address this longing directly. Ask yourself: Why am I holding on so tightly? What do I fear losing? What do I hope to gain?

Sometimes, we cling to a past friendship out of fear of loneliness or a loss of identity. We might idealize the past, forgetting the challenges. We might hope for reconciliation, believing it will magically solve everything.

These are illusions hindering healing and growth. You are not alone; your identity evolves, and happiness comes from within. You don’t need another person to complete you. Focus on the present, embrace yourself.

How to Cope with Longing: Accept the feeling without judgment, release the need to control the past, and transform the longing into something constructive. Acknowledge that some endings are beyond your control. Let go of regrets. Channel the pain into wisdom and compassion, allowing you to move forward with grace and peace.

Healthy Ways to Process Your Emotions:

Don’t bottle up or deny your feelings; they will only intensify. Avoid acting impulsively or harmfully. Instead:

Talk to trusted friends or family: Sharing your feelings can provide support and perspective.
Seek professional help: A therapist can offer guidance and coping strategies.
Journal your thoughts and feelings: Writing can help clarify your emotions.
Express yourself creatively: Engage in activities like art, music, or writing.

Cherishing Existing Bonds and Nurturing New Ones

In an ideal world, we’d have proper goodbyes and understand why friendships weaken. But life doesn’t always offer that luxury. Sometimes, friendships end abruptly.

Therefore, cherish the friendships you have today.

Communicate openly and honestly.
Listen actively and empathetically.
Celebrate each other’s presence and milestones.
Don’t take your friends for granted.
Resolve minor issues peacefully and respectfully.
Apologize when you’re wrong and forgive when you’re hurt.
Stay in touch despite distance or time.
Be happy for your friend’s successes and offer support during struggles.

Moving Forward: Remember that while the pain of losing a friend may linger, you will become stronger. New friendships will emerge, enriching your life. Embrace this journey, allow yourself to heal and grow with each step. You are not alone in these feelings; we all experience them.

With courage, patience, and self-compassion, you can overcome this pain. And always cherish the memories of those who touched your life, even if they are no longer present. They are part of your story, and you are part of theirs. There is gratitude in that connection. Thank you for listening.

Mark~

import random
import networkx as nx
import matplotlib.pyplot as plt
from nltk.sentiment import SentimentIntensityAnalyzer
from typing import List, Dict, Tuple

class User:
    """Represents a user in the social network."""

    def __init__(self, name: str):
        """Initialize a User instance."""
        self.name = name
        self.friends: Dict["User", int] = {}  # {friend_user: friendship_strength}
        self.posts: List[Dict[str, str]] = []  # [{'content': str, 'privacy': str, 'interests': List[str]}]
        self.messages: List[Dict[str, str]] = []  # [{'sender': User, 'content': str}]
        self.interests: set = set()

    def add_friendship(self, friend: "User", strength: int = 1):
        """Add a friend to the user's friend list."""
        self.friends[friend] = strength

    def remove_friendship(self, friend: "User"):
        """Remove a friend from the user's friend list."""
        if friend in self.friends:
            del self.friends[friend]

    def create_post(self, content: str, privacy: str = "public", interests: List[str] = None):
        """Create a post by the user."""
        self.posts.append({"content": content, "privacy": privacy, "interests": interests})

    def send_message(self, friend: "User", content: str):
        """Send a message to a friend."""
        friend.receive_message(self, content)

    def receive_message(self, sender: "User", content: str):
        """Receive a message from a friend."""
        self.messages.append({"sender": sender, "content": content})

class SocialNetwork:
    """Represents the social network."""

    def __init__(self):
        """Initialize the SocialNetwork instance."""
        self.users: Dict[str, User] = {}  # {username: User}

    def create_user(self, name: str) -> User:
        """Create a new user in the social network."""
        if name in self.users:
            return self.users[name]
        else:
            user = User(name)
            self.users[name] = user
            return user

    def add_friendship(self, user1: User, user2: User, strength1: int = 1, strength2: int = 1):
        """Add a friendship between two users."""
        user1.add_friendship(user2, strength1)
        user2.add_friendship(user1, strength2)

    def remove_friendship(self, user1: User, user2: User):
        """Remove a friendship between two users."""
        user1.remove_friendship(user2)
        user2.remove_friendship(user1)

    def find_longest_friendship(self) -> Tuple[User, User]:
        """Find the pair of users with the longest friendship connection (diameter of the largest component)."""
        if not self.users:
            return None

        # Get the largest connected component
        G = nx.Graph()
        for user in self.users.values():
            G.add_node(user.name)
            for friend in user.friends:
                G.add_edge(user.name, friend.name)
        largest_cc_nodes = max(nx.connected_components(G), key=len)
        largest_cc = G.subgraph(largest_cc_nodes)

        # Find the diameter of the largest connected component
        diameter = nx.diameter(largest_cc)
        periphery_nodes = nx.periphery(largest_cc)

        # Return an arbitrary pair of nodes from the periphery
        if periphery_nodes:
            user1_name = periphery_nodes[0]
            # Find the actual User objects
            user1 = self.users[user1_name]
            # Find a user furthest from user1 (one endpoint of the diameter)
            farthest_node_name = nx.shortest_path(largest_cc, source=user1_name)
            user2_name = max(farthest_node_name, key=lambda k: len(farthest_node_name[k]))
            user2 = self.users[user2_name]
            return user1, user2
        return None

    def suggest_friend(self, user: User):
        """Suggest a friend for a given user based on shared interests."""
        interests = {interest for post in user.posts if post.get('interests') for interest in post['interests']}
        potential_friends = {
            friend
            for friend in self.users.values()
            if friend != user
            and friend not in user.friends
            and friend.interests  # Ensure interests attribute exists and is not None
            and any(interest in interests for interest in friend.interests)
        }
        if potential_friends:
            suggested_friend = random.choice(list(potential_friends))
            print(f"We suggest {suggested_friend.name} as a friend for {user.name}.")
        else:
            print(f"We couldn't find any suitable friend suggestions for {user.name}.")

    def analyze_communities(self):
        """Analyze the network structure to identify communities."""
        G = nx.Graph()
        for user in self.users.values():
            G.add_node(user.name)
            for friend, strength in user.friends.items():
                G.add_edge(user.name, friend.name, weight=strength)

        communities_generator = nx.algorithms.community.greedy_modularity_communities(G)
        communities = list(communities_generator)

        for i, community in enumerate(communities, start=1):
            print(f"Community {i}: {', '.join(community)}")

        pos = nx.spring_layout(G)
        nx.draw(G, pos, with_labels=True)
        edge_labels = nx.get_edge_attributes(G, 'weight')
        nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
        plt.show()

    def search_users(self, query: str) -> List[User]:
        """Search for users within the network based on a query."""
        results = [
            user
            for user in self.users.values()
            if query.lower() in user.name.lower()
            or (user.interests and any(query.lower() == interest.lower() for interest in user.interests))
        ]
        if results:
            print(f"Search results for '{query}':")
            for result in results:
                print(result.name)
        else:
            print(f"No users found for '{query}'.")
        return results

    def analyze_activity_trends(self):
        """Analyze user activity based on the number of posts."""
        activity_counts = {user: len(user.posts) for user in self.users.values()}
        sorted_users = sorted(activity_counts.items(), key=lambda item: item[1], reverse=True)

        print("Activity trends:")
        for user, count in sorted_users:
            print(f"{user.name}: {count} posts")

    def analyze_sentiment(self):
        """Perform sentiment analysis on user posts."""
        sentiment_analyzer = SentimentIntensityAnalyzer()
        sentiment_scores = {}

        for user in self.users.values():
            sentiment_scores[user.name] = []
            for post in user.posts:
                scores = sentiment_analyzer.polarity_scores(post['content'])
                sentiment_scores[user.name].append(scores['compound'])

        average_scores = {user: sum(scores) / len(scores) if scores else 0 for user, scores in sentiment_scores.items()}

        print("Sentiment analysis:")
        for user, average_score in average_scores.items():
            print(f"{user}: Average sentiment score: {average_score:.2f}")

# Example usage
network = SocialNetwork()

alice = network.create_user("Alice")
bob = network.create_user("Bob")
carol = network.create_user("Carol")
dave = network.create_user("Dave")
eve = network.create_user("Eve")
frank = network.create_user("Frank")

network.add_friendship(alice, bob, strength1=3, strength2=2)
network.add_friendship(alice, carol, strength1=2, strength2=1)
network.add_friendship(alice, dave, strength1=1, strength2=1)
network.add_friendship(carol, dave, strength1=2, strength2=1)
network.add_friendship(dave, eve)
network.add_friendship(frank, dave, strength1=1, strength2=2)

network.remove_friendship(carol, dave)

longest_friends = network.find_longest_friendship()
if longest_friends:
    print(f"The longest friendship is between {longest_friends[0].name} and {longest_friends[1].name}.")

alice.create_post("Hello, everyone!", privacy='public', interests=['greeting'])
bob.create_post("Happy Friday!", privacy='public', interests=['weekend'])
carol.create_post("Just finished a great book!", privacy='friends', interests=['books'])
dave.create_post("Excited about my new project!", privacy='private', interests=['technology'])
eve.create_post("Enjoying the weekend!", privacy='public', interests=['weekend'])
frank.create_post("Great game last night!", privacy='public', interests=['sports'])

alice.send_message(bob, "Hey, how are you?")
bob.send_message(alice, "Doing great! How about you?")
alice.send_message(bob, "I'm good too. Let's catch up soon!")

network.suggest_friend(alice)
network.suggest_friend(carol)
network.suggest_friend(frank)

alice.interests = {"music", "technology"}
carol.interests = {"books", "art"}
frank.interests = {"technology", "sports"}

network.analyze_communities()

network.search_users("bob")
network.search_users("tech")

network.analyze_activity_trends()

network.analyze_sentiment()